Branson, S.[Steve],
van Horn, G.[Grant],
Wah, C.[Catherine],
Perona, P.[Pietro],
Belongie, S.J.[Serge J.],
The Ignorant Led by the Blind:
A Hybrid Human-Machine Vision System for Fine-Grained Categorization,
IJCV(108), No. 1-2, May 2014, pp. 3-29.
Springer DOI
1405
BibRef
Wah, C.[Catherine],
Maji, S.[Subhransu],
Belongie, S.J.[Serge J.],
Learning Localized Perceptual Similarity Metrics for Interactive
Categorization,
WACV15(502-509)
IEEE DOI
1503
Computer vision
BibRef
Wah, C.[Catherine],
van Horn, G.[Grant],
Branson, S.[Steve],
Maji, S.[Subhransu],
Perona, P.[Pietro],
Belongie, S.J.[Serge J.],
Similarity Comparisons for Interactive Fine-Grained Categorization,
CVPR14(859-866)
IEEE DOI
1409
BibRef
Branson, S.[Steve],
Perona, P.[Pietro],
Belongie, S.J.[Serge J.],
Strong supervision from weak annotation:
Interactive training of deformable part models,
ICCV11(1832-1839).
IEEE DOI
1201
Large scale learning of structured models. Interactive (semi-automated)
labeling and learning.
BibRef
Gosselin, P.H.[Philippe-Henri],
Murray, N.[Naila],
Jégou, H.[Hervé],
Perronnin, F.[Florent],
Revisiting the Fisher vector for fine-grained classification,
PRL(49), No. 1, 2014, pp. 92-98.
Elsevier DOI
1410
Computer vision
BibRef
Sfar, A.R.[Asma Rejeb],
Boujemaa, N.[Nozha],
Geman, D.[Donald],
Confidence Sets for Fine-Grained Categorization and Plant Species
Identification,
IJCV(111), No. 3, February 2015, pp. 255-275.
WWW Link.
1503
BibRef
Earlier:
Vantage Feature Frames for Fine-Grained Categorization,
CVPR13(835-842)
IEEE DOI
1309
among sub-categories (which bird?)
BibRef
Iscen, A.,
Tolias, G.[Giorgos],
Gosselin, P.,
Jegou, H.[Herve],
A Comparison of Dense Region Detectors for Image Search and
Fine-Grained Classification,
IP(24), No. 8, August 2015, pp. 2369-2381.
IEEE DOI
1505
Accuracy
BibRef
Deng, J.[Jia],
Krause, J.[Jonathan],
Stark, M.,
Fei-Fei, L.[Li],
Leveraging the Wisdom of the Crowd for Fine-Grained Recognition,
PAMI(38), No. 4, April 2016, pp. 666-676.
IEEE DOI
1603
BibRef
Earlier: A2, A3, A1, A4:
3D Object Representations for Fine-Grained Categorization,
3DRR13(554-561)
IEEE DOI
1403
BibRef
And: A1, A2, A4, Only:
Fine-Grained Crowdsourcing for Fine-Grained Recognition,
CVPR13(580-587)
IEEE DOI
1309
differences between classes very local.
Human in loop.
computational geometry.
Birds
See also Stanford Cars Dataset.
BibRef
Stark, M.[Michael],
Krause, J.[Jonathan],
Pepik, B.[Bojan],
Meger, D.[David],
Little, J.J.[James J.],
Schiele, B.[Bernt],
Koller, D.[Daphne],
Fine-Grained Categorization for 3D Scene Understanding,
BMVC12(36).
DOI Link
1301
BibRef
Krause, J.[Jonathan],
Gebru, T.[Timnit],
Deng, J.[Jia],
Li, L.J.[Li-Jia],
Fei-Fei, L.[Li],
Learning Features and Parts for Fine-Grained Recognition,
ICPR14(26-33)
IEEE DOI
1412
Detectors
BibRef
Zhang, Y.[Yu],
Wei, X.S.[Xiu-Shen],
Wu, J.X.[Jian-Xin],
Cai, J.F.[Jian-Fei],
Lu, J.B.[Jiang-Bo],
Nguyen, V.A.[Viet-Anh],
Do, M.N.[Minh N.],
Weakly Supervised Fine-Grained Categorization With Part-Based Image
Representation,
IP(25), No. 4, April 2016, pp. 1713-1725.
IEEE DOI
1604
Birds
BibRef
Nakayama, H.[Hideki],
Tsuda, T.[Tomoya],
Efficient Two-Step Middle-Level Part Feature Extraction for
Fine-Grained Visual Categorization,
IEICE(E99-D), No. 6, June 2016, pp. 1626-1634.
WWW Link.
1606
BibRef
Yao, H.,
Zhang, S.,
Zhang, Y.,
Li, J.,
Tian, Q.,
Coarse-to-Fine Description for Fine-Grained Visual Categorization,
IP(25), No. 10, October 2016, pp. 4858-4872.
IEEE DOI
1610
image classification
BibRef
Yao, H.,
Zhang, S.,
Yan, C.,
Zhang, Y.,
Li, J.,
Tian, Q.,
AutoBD: Automated Bi-Level Description for Scalable Fine-Grained
Visual Categorization,
IP(27), No. 1, January 2018, pp. 10-23.
IEEE DOI
1712
feature extraction, image classification, image representation,
image segmentation, Automated Bi-Level Description,
convolutional neural network
BibRef
Li, L.Y.[Ling-Yun],
Guo, Y.Q.[Yan-Qing],
Xie, L.X.[Ling-Xi],
Kong, X.W.[Xiang-Wei],
Tian, Q.[Qi],
Fine-grained visual categorization with fine-tuned segmentation,
ICIP15(2025-2029)
IEEE DOI
1512
Fine-Grained Visual Categorization
BibRef
Huang, C.,
He, Z.,
Cao, G.,
Cao, W.,
Task-Driven Progressive Part Localization for Fine-Grained Object
Recognition,
MultMed(18), No. 12, December 2016, pp. 2372-2383.
IEEE DOI
1612
BibRef
Earlier: A1, A2, Only:
Task-driven progressive part localization for fine-grained
recognition,
WACV16(1-9)
IEEE DOI
1606
Correlation
BibRef
Xu, Z.,
Tao, D.,
Huang, S.,
Zhang, Y.,
Friend or Foe: Fine-Grained Categorization With Weak Supervision,
IP(26), No. 1, January 2017, pp. 135-146.
IEEE DOI
1612
BibRef
Earlier: A3, A1, A3, A4:
Part-Stacked CNN for Fine-Grained Visual Categorization,
CVPR16(1173-1182)
IEEE DOI
1612
BibRef
Earlier: A1, A3, A4, A2:
Augmenting Strong Supervision Using Web Data for Fine-Grained
Categorization,
ICCV15(2524-2532)
IEEE DOI
1602
learning (artificial intelligence).
Computer architecture
BibRef
Xu, Z.,
Huang, S.,
Zhang, Y.,
Tao, D.,
Webly-Supervised Fine-Grained Visual Categorization via Deep Domain
Adaptation,
PAMI(40), No. 5, May 2018, pp. 1100-1113.
IEEE DOI
1804
Algorithm design and analysis, Flickr, Knowledge engineering,
Object recognition, Training, Training data, Visualization,
webly-supervised learning
BibRef
Huang, C.,
Li, H.,
Xie, Y.,
Wu, Q.,
Luo, B.,
PBC: Polygon-Based Classifier for Fine-Grained Categorization,
MultMed(19), No. 4, April 2017, pp. 673-684.
IEEE DOI
1704
Birds
BibRef
Kyaw, Z.,
Qi, S.,
Gao, K.,
Zhang, H.,
Zhang, L.,
Xiao, J.,
Wang, X.,
Chua, T.S.,
Matryoshka Peek: Toward Learning Fine-Grained, Robust, Discriminative
Features for Product Search,
MultMed(19), No. 6, June 2017, pp. 1272-1284.
IEEE DOI
1705
Birds, Buildings, Convolution, Manuals, Robustness, Search problems,
Training, Feature extraction, image representation,
image retrieval, robust, learning
BibRef
Wei, X.S.,
Luo, J.H.,
Wu, J.,
Zhou, Z.H.,
Selective Convolutional Descriptor Aggregation for Fine-Grained Image
Retrieval,
IP(26), No. 6, June 2017, pp. 2868-2881.
IEEE DOI
1705
feature extraction, image classification, image retrieval,
neural nets, SCDA feature visualization, SCDA method,
deep convolutional neural network model,
fine-grained image retrieval, imageNet classification,
selective convolutional descriptor aggregation,
selective convolutional descriptor aggregation method,
state-of-the-art general image retrieval approach,
unsupervised retrieval task, visual attribute, Automobiles, Birds,
Buildings, Convolution, Dogs, Image retrieval, Machine learning,
Fine-grained image retrieval, selection and aggregation,
unsupervised, object, localization
BibRef
Zhao, B.[Bo],
Wu, X.[Xiao],
Feng, J.S.[Jia-Shi],
Peng, Q.[Qiang],
Yan, S.C.[Shui-Cheng],
Diversified Visual Attention Networks for Fine-Grained Object
Classification,
MultMed(19), No. 6, June 2017, pp. 1245-1256.
IEEE DOI
1705
BibRef
Earlier: A1, A3, A2, A5, Only:
Memory-Augmented Attribute Manipulation Networks for Interactive
Fashion Search,
CVPR17(6156-6164)
IEEE DOI
1711
Birds, Diversity reception, Dogs, Feature extraction,
Predictive models, Training, Visualization, Deep learning,
fine-grained object classification,
long-short-term-memory (LSTM), visual, attention.
Clothing, Image color analysis,
Image representation, Prototypes, Search engines, Visualization
BibRef
Huang, Z.H.[Zhen-Huan],
Duan, X.Y.[Xiao-Yue],
Zhao, B.[Bo],
Lü, J.H.[Jin-Hu],
Zhang, B.C.[Bao-Chang],
Interpretable Attention Guided Network for Fine-grained Visual
Classification,
MLCSA20(52-63).
Springer DOI
2103
BibRef
Xie, G.S.[Guo-Sen],
Zhang, X.Y.[Xu-Yao],
Yang, W.H.[Wen-Han],
Xu, M.L.[Ming-Liang],
Yan, S.C.[Shui-Cheng],
Liu, C.L.[Cheng-Lin],
LG-CNN: From local parts to global discrimination for fine-grained
recognition,
PR(71), No. 1, 2017, pp. 118-131.
Elsevier DOI
1707
Fine-grained, recognition
BibRef
Ge, H.[Hao],
Yang, F.[Feng],
Tu, X.G.[Xiao-Guang],
Xie, M.[Mei],
Ma, Z.[Zheng],
Pre-Processing for Fine-Grained Image Classification,
IEICE(E100-D), No. 8, August 2017, pp. 1938-1942.
WWW Link.
1708
BibRef
Sun, T.[Ting],
Sun, L.[Lin],
Yeung, D.Y.[Dit-Yan],
Fine-grained categorization via CNN-based automatic extraction and
integration of object-level and part-level features,
IVC(64), No. 1, 2017, pp. 47-66.
Elsevier DOI
1708
Fine-grained categorization
BibRef
Meng, Z.J.[Zhi-Jun],
Wang, Y.[Yan],
Wu, X.Y.[Xin-Yu],
Yin, Y.T.[Ya-Ting],
Li, T.[Teng],
Contextual aerial image categorization using codebook,
JVCIR(48), No. 1, 2017, pp. 404-410.
Elsevier DOI
1708
topology based codebook.
Aerial image
BibRef
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.[Anton],
Cross-Convolutional-Layer Pooling for Image Recognition,
PAMI(39), No. 11, November 2017, pp. 2305-2313.
IEEE DOI
1710
Computational efficiency, Feature extraction, Image recognition,
Image representation, Image retrieval, Neural networks,
Visualization, Convolutional networks, deep learning,
fine-grained object recognition,
BibRef
Lin, T.Y.,
Roy Chowdhury, A.,
Maji, S.,
Bilinear Convolutional Neural Networks for Fine-Grained Visual
Recognition,
PAMI(40), No. 6, June 2018, pp. 1309-1322.
IEEE DOI
1805
BibRef
Earlier:
Bilinear CNN Models for Fine-Grained Visual Recognition,
ICCV15(1449-1457)
IEEE DOI
1602
Birds, Computer architecture, Convolutional codes,
Feature extraction, Image recognition, Neural networks,
texture representations
Atmospheric modeling
BibRef
Kuncheva, L.I.[Ludmila I.],
Constance, J.H.V.[James H.V.],
Restricted Set Classification with prior probabilities:
A case study on chessboard recognition,
PRL(111), 2018, pp. 36-42.
Elsevier DOI
1808
Classification methodology, Restricted Set Classification,
Simultaneous classification, Image recognition, Chess piece recognition
BibRef
Slayback, D.T.,
Files, B.T.,
Lance, B.J.,
Brooks, J.R.,
Effects of Image Presentation Highlighting and Accuracy on Target
Category Learning,
HMS(48), No. 4, August 2018, pp. 400-407.
IEEE DOI
1808
learning (artificial intelligence),
visual perception, target category learning,
information processing
BibRef
Yang, Z.[Ze],
Luo, T.[Tiange],
Wang, D.[Dong],
Hu, Z.Q.[Zhi-Qiang],
Gao, J.[Jun],
Wang, L.W.[Li-Wei],
Learning to Navigate for Fine-Grained Classification,
ECCV18(XIV: 438-454).
Springer DOI
1810
BibRef
Yao, Y.Z.[Ya-Zhou],
Yang, W.K.[Wan-Kou],
Huang, P.[Pu],
Wang, Q.[Qiong],
Cai, Y.[Yunfei],
Tang, Z.[Zhenmin],
Exploiting textual and visual features for image categorization,
PRL(117), 2019, pp. 140-145.
Elsevier DOI
1901
General corpus information, Image categorization, Web-supervised
BibRef
Cai, D.D.[Ding-Ding],
Chen, K.[Ke],
Qian, Y.L.[Yan-Lin],
Kämäräinen, J.K.[Joni-Kristian],
Convolutional low-resolution fine-grained classification,
PRL(119), 2019, pp. 166-171.
Elsevier DOI
1902
Fine-grained image classification,
Super resolution convoluational neural networks, Deep learning
BibRef
Wang, Y.F.[Ya-Fei],
Wang, Z.P.[Ze-Peng],
A survey of recent work on fine-grained image classification
techniques,
JVCIR(59), 2019, pp. 210-214.
Elsevier DOI
1903
Image classification, Deep learning, Convolutional neural networks
BibRef
Wu, L.[Lin],
Wang, Y.[Yang],
Li, X.[Xue],
Gao, J.B.[Jun-Bin],
Deep Attention-Based Spatially Recursive Networks for Fine-Grained
Visual Recognition,
Cyber(49), No. 5, May 2019, pp. 1791-1802.
IEEE DOI
1903
Feature extraction, Visualization, Task analysis, Detectors, Birds,
Encoding, Computational modeling, Bilinear pooling,
visual attention
BibRef
Baffour, A.A.[Adu Asare],
Qin, Z.[Zhen],
Wang, Y.[Yong],
Qin, Z.G.[Zhi-Guang],
Choo, K.K.R.[Kim-Kwang Raymond],
Spatial Self-Attention Network with Self-Attention Distillation for
Fine-Grained Image Recognition,
JVCIR(81), 2021, pp. 103368.
Elsevier DOI
2112
Fine-grained recognition, Spatial self-attention,
Knowledge distillation, Convolutional neural network
BibRef
Qi, L.[Lei],
Lu, X.Q.[Xiao-Qiang],
Li, X.L.[Xue-Long],
Exploiting spatial relation for fine-grained image classification,
PR(91), 2019, pp. 47-55.
Elsevier DOI
1904
Fine-grained image classification, Spatial relation,
Convolutional neural network
BibRef
He, X.T.[Xiang-Teng],
Peng, Y.X.[Yu-Xin],
Zhao, J.J.[Jun-Jie],
Fast Fine-Grained Image Classification via Weakly Supervised
Discriminative Localization,
CirSysVideo(29), No. 5, May 2019, pp. 1394-1407.
IEEE DOI
1905
BibRef
Earlier: A1, A2, Only:
Fine-Grained Image Classification via Combining Vision and Language,
CVPR17(7332-7340)
IEEE DOI
1711
Proposals, Object detection, Feature extraction, Training, Detectors,
Machine learning,
multi-level attention.
Image recognition, Natural languages,
Semantics, Streaming media, Visualization
BibRef
He, X.T.[Xiang-Teng],
Peng, Y.X.[Yu-Xin],
Zhao, J.J.[Jun-Jie],
Which and How Many Regions to Gaze:
Focus Discriminative Regions for Fine-Grained Visual Categorization,
IJCV(127), No. 9, September 2019, pp. 1235-1255.
Springer DOI
1908
BibRef
Flores, C.F.[Carola Figueroa],
Gonzalez-Garcia, A.[Abel],
van de Weijer, J.[Joost],
Raducanu, B.[Bogdan],
Saliency for fine-grained object recognition in domains with scarce
training data,
PR(94), 2019, pp. 62-73.
Elsevier DOI
1906
Object recognition, Fine-grained classification,
Saliency detection, Scarce training data
BibRef
Hoffmann, E.J.[Eike Jens],
Wang, Y.Y.[Yuan-Yuan],
Werner, M.[Martin],
Kang, J.[Jian],
Zhu, X.X.[Xiao Xiang],
Model Fusion for Building Type Classification from Aerial and Street
View Images,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Zhu, Y.,
Deng, X.,
Newsam, S.,
Fine-Grained Land Use Classification at the City Scale Using
Ground-Level Images,
MultMed(21), No. 7, July 2019, pp. 1825-1838.
IEEE DOI
1906
Training, Urban areas, Buildings, Convolutional neural networks,
Videos, Streaming media, Task analysis, Geo-referenced images,
proximate sensing
BibRef
Wang, R.G.[Rong-Gui],
Yao, X.C.[Xu-Chen],
Yang, J.[Juan],
Xue, L.X.[Li-Xia],
Hu, M.[Min],
Hierarchical deep transfer learning for finie-grained categorization
on micro datasets,
JVCIR(62), 2019, pp. 129-139.
Elsevier DOI
1908
Fine-grained categorization, Convolutional neural network,
Transfer learning, Multi-task learning, Model compression
BibRef
Wei, X.,
Wang, P.,
Liu, L.,
Shen, C.,
Wu, J.,
Piecewise Classifier Mappings: Learning Fine-Grained Learners for
Novel Categories With Few Examples,
IP(28), No. 12, December 2019, pp. 6116-6125.
IEEE DOI
1909
Image recognition, Task analysis, Training, Birds, Learning systems,
Computational modeling,
learning to learn
BibRef
Zheng, H.,
Fu, J.,
Zha, Z.,
Luo, J.,
Mei, T.,
Learning Rich Part Hierarchies With Progressive Attention Networks
for Fine-Grained Image Recognition,
IP(29), No. 1, 2020, pp. 476-488.
IEEE DOI
1910
convolutional neural nets, image recognition,
learning (artificial intelligence), optimisation,
progressive attention
BibRef
Wang, Y.H.[Yan-Hai],
Li, Q.Q.[Qing-Quan],
Chen, B.[Bo],
Image classification towards transmission line fault detection via
learning deep quality-aware fine-grained categorization,
JVCIR(64), 2019, pp. 102647.
Elsevier DOI
1911
Fine-grained categorization, Fault recognition, Quality model,
Fast R-CNN, SVM
BibRef
Wang, J.[Jiang],
Song, Y.[Yang],
Leung, T.[Thomas],
Rosenberg, C.[Chuck],
Wang, J.B.[Jing-Bin],
Philbin, J.[James],
Chen, B.[Bo],
Wu, Y.[Ying],
Learning Fine-Grained Image Similarity with Deep Ranking,
CVPR14(1386-1393)
IEEE DOI
1409
BibRef
Liu, D.C.[Di-Chao],
Wang, Y.[Yu],
Kato, J.[Jien],
Attention-Guided Spatial Transformer Networks for Fine-Grained Visual
Recognition,
IEICE(E102-D), No. 12, December 2019, pp. 2577-2586.
WWW Link.
1912
BibRef
Zeng, X.X.[Xian-Xian],
Zhang, Y.[Yun],
Wang, X.D.[Xiao-Dong],
Chen, K.R.[Kai-Rui],
Li, D.[Dong],
Yang, W.J.[Wei-Jun],
Fine-Grained Image Retrieval via Piecewise Cross Entropy loss,
IVC(93), 2020, pp. 103820.
Elsevier DOI
2001
Fine-Grained Image Retrieval, CNN, Piecewise cross entropy loss
BibRef
Rodríguez, P.,
Velazquez, D.,
Cucurull, G.,
Gonfaus, J.M.,
Roca, F.X.,
Gonzŕlez, J.,
Pay Attention to the Activations: A Modular Attention Mechanism for
Fine-Grained Image Recognition,
MultMed(22), No. 2, February 2020, pp. 502-514.
IEEE DOI
2001
Computer architecture, Computational modeling, Image recognition,
Task analysis, Proposals, Logic gates, Clutter, Image Retrieval Deep Learning Convolutional Neural Networks Attention-based Learning
BibRef
Chang, D.L.[Dong-Liang],
Ding, Y.F.[Yi-Feng],
Xie, J.Y.[Ji-Yang],
Bhunia, A.K.[Ayan Kumar],
Li, X.X.[Xiao-Xu],
Ma, Z.Y.[Zhan-Yu],
Wu, M.[Ming],
Guo, J.[Jun],
Song, Y.Z.[Yi-Zhe],
The Devil is in the Channels:
Mutual-Channel Loss for Fine-Grained Image Classification,
IP(29), 2020, pp. 4683-4695.
IEEE DOI
2003
Feature extraction, Training, Visualization, Automobiles,
Task analysis, Data mining, Manuals, mutual channel
BibRef
Min, S.B.[Shao-Bo],
Yao, H.T.[Han-Tao],
Xie, H.T.[Hong-Tao],
Zha, Z.J.[Zheng-Jun],
Zhang, Y.D.[Yong-Dong],
Multi-Objective Matrix Normalization for Fine-Grained Visual
Recognition,
IP(29), 2020, pp. 4996-5009.
IEEE DOI
2003
Visualization, Graphics processing units, Feature extraction,
Convergence, Optimization, Covariance matrices, Training,
multi-objective optimization
BibRef
Nauata, N.[Nelson],
Hu, H.X.[He-Xiang],
Zhou, G.T.[Guang-Tong],
Deng, Z.W.[Zhi-Wei],
Liao, Z.C.[Zi-Cheng],
Mori, G.[Greg],
Structured Label Inference for Visual Understanding,
PAMI(42), No. 5, May 2020, pp. 1257-1271.
IEEE DOI
2004
Videos, Visualization, Task analysis, Hidden Markov models,
Deep learning, Feature extraction, Neural networks,
structured inference
BibRef
Jin, S.,
Yao, H.,
Sun, X.,
Zhou, S.,
Zhang, L.,
Hua, X.,
Deep Saliency Hashing for Fine-Grained Retrieval,
IP(29), 2020, pp. 5336-5351.
IEEE DOI
2004
Semantics, Quantization (signal), Loss measurement, Task analysis,
Sun, Birds, Training, Fine-grained retrieval,
salient region mining
BibRef
Shi, H.,
Tao, L.,
Fine-Grained Visual Comparison Based on Relative Attribute Quadratic
Discriminant Analysis,
SMCS(50), No. 6, June 2020, pp. 2113-2119.
IEEE DOI
2005
Visualization, Measurement, Feature extraction, Learning systems,
Semantics, Training, Predictive models, Local ranking,
visual comparison
BibRef
Nitta, N.[Naoko],
Nakamura, K.[Kazuaki],
Babaguchi, N.[Noboru],
Constructing Geospatial Concept Graphs from Tagged Images for
Geo-Aware Fine-Grained Image Recognition,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Liu, C.,
Xie, H.,
Zha, Z.,
Yu, L.,
Chen, Z.,
Zhang, Y.,
Bidirectional Attention-Recognition Model for Fine-Grained Object
Classification,
MultMed(22), No. 7, July 2020, pp. 1785-1795.
IEEE DOI
2007
Feature extraction, Proposals, Annotations, Visualization,
Task analysis, Training, Computational modeling,
data augmentation
BibRef
Wang, Q.,
Liu, X.,
Liu, W.,
Liu, A.,
Liu, W.,
Mei, T.,
MetaSearch: Incremental Product Search via Deep Meta-Learning,
IP(29), 2020, pp. 7549-7564.
IEEE DOI
2007
Product search, Few-shot learning, Incremental search,
Meta-learning, Multipooling
BibRef
Song, K.,
Wei, X.,
Shu, X.,
Song, R.,
Lu, J.,
Bi-Modal Progressive Mask Attention for Fine-Grained Recognition,
IP(29), 2020, pp. 7006-7018.
IEEE DOI
2007
Visualization, Image recognition, Feature extraction, Annotations,
Task analysis, Semantics, Streaming media,
language modality
BibRef
Luo, W.,
Zhang, H.,
Li, J.,
Wei, X.S.,
Learning Semantically Enhanced Feature for Fine-Grained Image
Classification,
SPLetters(27), 2020, pp. 1545-1549.
IEEE DOI
2009
Semantics, Training, Birds, Feature extraction, Correlation, Entropy,
Dogs, Image classification, visual categorization, feature learning
BibRef
Sun, K.[Kangbo],
Zhu, J.[Jie],
A Two-Stage Approach for Fine-Grained Visual Recognition via Confidence
Ranking and Fusion,
IEICE(E103-D), No. 12, December 2020, pp. 2693-2700.
WWW Link.
2012
BibRef
Li, X.,
Wu, J.,
Sun, Z.,
Ma, Z.,
Cao, J.,
Xue, J.H.,
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image
Classification,
IP(30), 2021, pp. 1318-1331.
IEEE DOI
2012
Task analysis, Prototypes, Training, Neural networks,
Extraterrestrial measurements, Euclidean distance, Sun,
metric learning
BibRef
Hu, Y.T.[Yu-Tao],
Yang, Y.D.[Yan-Dan],
Zhang, J.[Jun],
Cao, X.B.[Xian-Bin],
Zhen, X.T.[Xian-Tong],
Attentional Kernel Encoding Networks for Fine-Grained Visual
Categorization,
CirSysVideo(31), No. 1, January 2021, pp. 301-314.
IEEE DOI
2101
Encoding, Feature extraction, Kernel, Visualization, Image coding,
Task analysis, Layout, Fine-grained visual categorization,
attention
BibRef
Zhang, L.[Lei],
Liu, W.H.[Wen-Hui],
Xiang, X.Z.[Xue-Zhi],
Sun, Y.[Yan],
Zhen, X.T.[Xian-Tong],
Learning discriminant grassmann kernels for image-set classification,
ICIP17(4477-4481)
IEEE DOI
1803
Covariance matrices, Kernel, Manifolds, Measurement,
Task analysis, Training, Discriminant Grassmann kernel,
oPartial Grassmann kernel
BibRef
Sun, H.L.[Hao-Liang],
Zhen, X.T.[Xian-Tong],
Zheng, Y.J.[Yuan-Jie],
Yang, G.P.[Gong-Ping],
Yin, Y.L.[Yi-Long],
Li, S.[Shuo],
Learning Deep Match Kernels for Image-Set Classification,
CVPR17(6240-6249)
IEEE DOI
1711
Aggregates, Covariance matrices, Kernel, Manifolds,
Neural, networks
BibRef
Wang, Y.B.[Yun-Bo],
Ou, X.F.[Xian-Feng],
Liang, J.[Jian],
Sun, Z.A.[Zhen-An],
Deep Semantic Reconstruction Hashing for Similarity Retrieval,
CirSysVideo(31), No. 1, January 2021, pp. 387-400.
IEEE DOI
2101
Semantics, Quantization (signal), Binary codes,
Image reconstruction, Hamming distance, Marine vehicles, Airplanes,
similarity retrieval
BibRef
Ding, Y.,
Ma, Z.,
Wen, S.,
Xie, J.,
Chang, D.,
Si, Z.,
Wu, M.,
Ling, H.,
AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural
Network for Fine-Grained Visual Classification,
IP(30), 2021, pp. 2826-2836.
IEEE DOI
2102
Task analysis, Feature extraction, Visualization, Annotations,
Semantics, Proposals, Birds, Fine-grained visual classification,
deep learning
BibRef
Palazzo, S.,
Murabito, F.,
Pino, C.,
Rundo, F.,
Giordano, D.,
Shah, M.,
Spampinato, C.,
Exploiting structured high-level knowledge for domain-specific visual
classification,
PR(112), 2021, pp. 107806.
Elsevier DOI
2102
Fine-grained visual classification, Computational ontologies, Belief networks
BibRef
Chen, J.M.[Jia-Min],
Hu, J.G.[Jian-Guo],
Li, S.R.[Shi-Ren],
Learning to locate for fine-grained image recognition,
CVIU(206), 2021, pp. 103184.
Elsevier DOI
2104
Background suppression, CNN, Feature extraction,
Fine-grained image recognition, Salient point detection, Weakly supervised
BibRef
Liu, H.M.[Hao-Miao],
Wang, R.P.[Rui-Ping],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
What is a Tabby? Interpretable Model Decisions by Learning
Attribute-Based Classification Criteria,
PAMI(43), No. 5, May 2021, pp. 1791-1807.
IEEE DOI
2104
Cats, Prototypes, Visualization, Task analysis, Streaming media,
Predictive models, Scalability, Interpretable model,
classification criteria
BibRef
He, G.Q.[Gui-Qing],
Li, F.[Feng],
Wang, Q.Y.[Qi-Yao],
Bai, Z.W.[Zong-Wen],
Xu, Y.L.[Yue-Lei],
A hierarchical sampling based triplet network for fine-grained image
classification,
PR(115), 2021, pp. 107889.
Elsevier DOI
2104
Metric learning, Triplet network, Layered ontology,
Layered triplet loss, Multi-task learning
BibRef
Zheng, X.T.[Xiang-Tao],
Qi, L.[Lei],
Ren, Y.[Yutao],
Lu, X.Q.[Xiao-Qiang],
Fine-Grained Visual Categorization by Localizing Object Parts With
Single Image,
MultMed(23), 2021, pp. 1187-1199.
IEEE DOI
2105
Feature extraction, Detectors, Training, Image representation,
Visualization, Semantics, Birds,
Dropout learning
BibRef
Xu, Q.Q.[Qian-Qian],
Xiong, J.C.[Jie-Chao],
Cao, X.C.[Xiao-Chun],
Huang, Q.M.[Qing-Ming],
Yao, Y.[Yuan],
Evaluating Visual Properties via Robust HodgeRank,
IJCV(129), No. 5, May 2021, pp. 1732-1753.
Springer DOI
2105
What properties help.
BibRef
Niu, Y.[Yi],
Jiao, Y.[Yang],
Shi, G.M.[Guang-Ming],
Attention-shift based deep neural network for fine-grained visual
categorization,
PR(116), 2021, pp. 107947.
Elsevier DOI
2106
Fine-grained visual categorization, Deep neural network,
Human perception mechanism, Attention-shift, Encoder-decoder
BibRef
Zhang, C.J.[Chun-Jie],
Wang, D.H.[Da-Han],
Li, H.S.[Hai-Sheng],
Discriminative semantic region selection for fine-grained recognition,
JVCIR(77), 2021, pp. 103084.
Elsevier DOI
2106
Fine-grained recognition, Discriminative region selection,
Semantic correlation, Object categorization
BibRef
Yu, X.H.[Xiao-Han],
Zhao, Y.[Yang],
Gao, Y.S.[Yong-Sheng],
Xiong, S.W.[Sheng-Wu],
MaskCOV: A random mask covariance network for ultra-fine-grained
visual categorization,
PR(119), 2021, pp. 108067.
Elsevier DOI
2106
Ultra-fine-grained visual categorization,
Fine-grained visual categorization, Covariance matrix, Self-supervised learning
BibRef
Nasrabadi, M.S.[Mohammad Sohrabi],
Safabakhsh, R.[Reza],
3D object recognition with a linear time-varying system of overlay
layers,
IET-CV(15), No. 5, 2021, pp. 380-391.
DOI Link
2107
Gene regulatory networks.
BibRef
Yang, Z.[Zhen],
Wang, Z.P.[Zhi-Peng],
Luo, L.K.[Ling-Kun],
Gan, H.P.[Hong-Ping],
Zhang, T.[Tao],
SWS-DAN: Subtler WS-DAN for fine-grained image classification,
JVCIR(79), 2021, pp. 103245.
Elsevier DOI
2109
Fine-grained, Classification, WS-DAN, SWS-DAN, Data augmentation, Loss function
BibRef
Zhang, L.B.[Lian-Bo],
Huang, S.L.[Shao-Li],
Liu, W.[Wei],
Learning sequentially diversified representations for fine-grained
categorization,
PR(121), 2022, pp. 108219.
Elsevier DOI
2109
Fine-grained visual categorization,
Convolutional neural networks, Diversity learning, Object recognition
BibRef
Fu, H.[Huan],
Jia, R.F.[Rong-Fei],
Gao, L.[Lin],
Gong, M.M.[Ming-Ming],
Zhao, B.Q.[Bin-Qiang],
Maybank, S.J.[Steve J.],
Tao, D.C.[Da-Cheng],
3D-FUTURE: 3D Furniture Shape with TextURE,
IJCV(129), No. 12, December 2021, pp. 3313-3337.
Springer DOI
2111
Dataset Furniture.
WWW Link.
BibRef
Zhao, P.P.[Pei-Pei],
Miao, Q.G.[Qi-Guang],
Yao, H.[Hang],
Liu, X.Z.[Xiang-Zeng],
Liu, R.[Ruyi],
Gong, M.[Maoguo],
CA-PMG: Channel attention and progressive multi-granularity training
network for fine-grained visual classification,
IET-IPR(15), No. 14, 2021, pp. 3718-3727.
DOI Link
2112
BibRef
Zhao, Y.F.[Yi-Fan],
Li, J.[Jia],
Chen, X.[Xiaowu],
Tian, Y.H.[Yong-Hong],
Part-Guided Relational Transformers for Fine-Grained Visual
Recognition,
IP(30), 2021, pp. 9470-9481.
IEEE DOI
2112
Transformers, Visualization, Correlation, Task analysis,
Feature extraction, Semantics, Costs,
relationship
BibRef
Xiang, X.G.[Xin-Guang],
Zhang, Y.J.[Ya-Jie],
Jin, L.[Lu],
Li, Z.C.[Ze-Chao],
Tang, J.H.[Jin-Hui],
Sub-Region Localized Hashing for Fine-Grained Image Retrieval,
IP(31), 2022, pp. 314-326.
IEEE DOI
2112
Codes, Feature extraction, Location awareness,
Representation learning, Image retrieval, Semantics, Dispersion,
binary centers
BibRef
Yu, J.[Jun],
Tan, M.[Min],
Zhang, H.Y.[Hong-Yuan],
Rui, Y.[Yong],
Tao, D.C.[Da-Cheng],
Hierarchical Deep Click Feature Prediction for Fine-Grained Image
Recognition,
PAMI(44), No. 2, February 2022, pp. 563-578.
IEEE DOI
2201
Visualization, Feature extraction, Image recognition, Semantics,
Predictive models, Vocabulary, Task analysis, Click prediction,
transfer learning
BibRef
Han, J.W.[Jun-Wei],
Yao, X.[Xiwen],
Cheng, G.[Gong],
Feng, X.X.[Xiao-Xu],
Xu, D.[Dong],
P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained
Visual Categorization,
PAMI(44), No. 2, February 2022, pp. 579-590.
IEEE DOI
2201
Visualization, Training, Detectors, Streaming media, Measurement,
Feature extraction, Convolutional neural networks,
fine-grained visual categorization
BibRef
Koniusz, P.[Piotr],
Zhang, H.G.[Hong-Guang],
Power Normalizations in Fine-Grained Image, Few-Shot Image and Graph
Classification,
PAMI(44), No. 2, February 2022, pp. 591-609.
IEEE DOI
2201
Feature extraction, Covariance matrices, Visualization, Training,
Laplace equations, Pipelines, Eigenvalues and eigenfunctions, CNN,
heat diffusion
BibRef
Wang, C.Q.[Chao-Qing],
Qian, Y.[Yurong],
Gong, W.J.[Wei-Jun],
Cheng, J.[Junjong],
Wang, Y.Q.[Yong-Qiang],
Wang, Y.[Yuefei],
Cross-layer progressive attention bilinear fusion method for
fine-grained visual classification,
JVCIR(82), 2022, pp. 103414.
Elsevier DOI
2201
Fine-grained visual classification, Feature fusion, Attention, Progressive
BibRef
Liao, Q.Y.[Qi-Yu],
Wang, D.D.[Da-Dong],
Xu, M.[Min],
Category attention transfer for efficient fine-grained visual
categorization,
PRL(153), 2022, pp. 10-15.
Elsevier DOI
2201
Fine-grained classification, Attention transfering, Efficient computation
BibRef
Sun, X.[Xian],
Wang, P.J.[Pei-Jin],
Yan, Z.Y.[Zhi-Yuan],
Xu, F.[Feng],
Wang, R.P.[Rui-Ping],
Diao, W.H.[Wen-Hui],
Chen, J.[Jin],
Li, J.[Jihao],
Feng, Y.C.[Ying-Chao],
Xu, T.[Tao],
Weinmann, M.[Martin],
Hinz, S.[Stefan],
Wang, C.[Cheng],
Fu, K.[Kun],
FAIR1M: A benchmark dataset for fine-grained object recognition in
high-resolution remote sensing imagery,
PandRS(184), 2022, pp. 116-130.
Elsevier DOI
2202
Remote sensing images,
Fine-grained object detection and recognition, Deep learning,
Convolutional neural network (CNN)
BibRef
Wu, Q.[Qin],
Miao, S.T.[Shu-Ting],
Chai, Z.L.[Zhi-Lei],
Guo, G.D.[Guo-Dong],
Fine-Grained Image Classification With Global Information and
Adaptive Compensation Loss,
SPLetters(29), 2022, pp. 36-40.
IEEE DOI
2202
Crops, Feature extraction, Task analysis, Image classification,
Mathematical models, Data mining, Classification algorithms,
global information
BibRef
Chen, Z.D.[Zhen-Duo],
Luo, X.[Xin],
Wang, Y.X.[Yong-Xin],
Guo, S.Q.[Shan-Qing],
Xu, X.S.[Xin-Shun],
Fine-Grained Hashing With Double Filtering,
IP(31), 2022, pp. 1671-1683.
IEEE DOI
2202
Feature extraction, Filtering, Fish, Binary codes, Training,
Neural networks, Hash functions, Learning to hash,
supervised hashing
BibRef
Huang, H.X.[Hua-Xi],
Zhang, J.J.[Jun-Jie],
Yu, L.T.[Li-Tao],
Zhang, J.[Jian],
Wu, Q.[Qiang],
Xu, C.[Chang],
TOAN: Target-Oriented Alignment Network for Fine-Grained Image
Categorization With Few Labeled Samples,
CirSysVideo(32), No. 2, February 2022, pp. 853-866.
IEEE DOI
2202
Feature extraction, Task analysis, Visualization, Training,
Optical fibers, Computational modeling, Phase change materials,
second-order relation extraction
BibRef
Tan, Y.[Yanhao],
Rahman, M.M.[Mohammad Muntasir],
Yan, Y.[Yanfu],
Xue, J.[Jian],
Shao, L.[Ling],
Lu, K.[Ke],
Fine-Grained Categorization From RGB-D Images,
MultMed(24), 2022, pp. 917-928.
IEEE DOI
2202
Dogs, Sensors, Automobiles, Birds, Benchmark testing, Task analysis,
Image color analysis, Deep convolutional neural network, RGB-D dataset
BibRef
Liu, H.F.[Hua-Feng],
Zhang, C.Y.[Chuan-Yi],
Yao, Y.Z.[Ya-Zhou],
Wei, X.S.[Xiu-Shen],
Shen, F.M.[Fu-Min],
Tang, Z.M.[Zhen-Min],
Zhang, J.[Jian],
Exploiting Web Images for Fine-Grained Visual Recognition by
Eliminating Open-Set Noise and Utilizing Hard Examples,
MultMed(24), 2022, pp. 546-557.
IEEE DOI
2202
Noise measurement, Training, Visualization, Image recognition,
Annotations, Robustness, Data models, Noisy web images,
fine-grained recognition
BibRef
Liu, D.C.[Di-Chao],
Wang, Y.[Yu],
Mase, K.J.[Ken-Ji],
Kato, J.[Jien],
Recursive Multi-Scale Channel-Spatial Attention for Fine-Grained Image
Classification,
IEICE(E105-D), No. 3, March 2022, pp. 713-726.
WWW Link.
2203
BibRef
Earlier:
Attention-Based Multi-Task Learning for Fine-Grained Image
Classification,
ICIP21(1499-1503)
IEEE DOI
2201
Image processing, Regulation, Agriculture,
Convolutional neural networks, Automobiles, Task analysis, Attention Learning
BibRef
Liu, M.[Man],
Zhang, C.J.[Chun-Jie],
Bai, H.[Huihui],
Zhang, R.[Riquan],
Zhao, Y.[Yao],
Cross-Part Learning for Fine-Grained Image Classification,
IP(31), 2022, pp. 748-758.
IEEE DOI
2201
Transformers, Location awareness, Task analysis, Proposals,
Feature extraction, Navigation, Computer architecture, transformer
BibRef
Sun, K.[Kangbo],
Zhu, J.[Jie],
Searching and Learning Discriminative Regions for Fine-Grained Image
Retrieval and Classification,
IEICE(E105-D), No. 1, January 2022, pp. 141-149.
WWW Link.
2201
BibRef
Ding, Y.[Yao],
Han, Z.J.[Zhen-Jun],
Zhou, Y.Z.[Yan-Zhao],
Zhu, Y.[Yi],
Chen, J.[Jie],
Ye, Q.X.[Qi-Xiang],
Jiao, J.B.[Jian-Bin],
Dynamic Perception Framework for Fine-Grained Recognition,
CirSysVideo(32), No. 3, March 2022, pp. 1353-1365.
IEEE DOI
2203
Kernel, Convolution, Feature extraction, Visualization,
Image recognition, Task analysis, Radio frequency,
fine-grained recognition
BibRef
Liu, H.F.[Hua-Feng],
Zhang, H.F.[Hao-Feng],
Lu, J.F.[Jian-Feng],
Tang, Z.M.[Zhen-Min],
Exploiting Web Images for Fine-Grained Visual Recognition via Dynamic
Loss Correction and Global Sample Selection,
MultMed(24), 2022, pp. 1105-1115.
IEEE DOI
2203
Noise measurement, Training, Training data, Uncertainty, History,
Feature extraction, Visualization, Fine-grained recognition,
uncertainly-based dynamic loss correction
BibRef
Ke, X.[Xiao],
Huang, Y.Y.[Yan-Yan],
Guo, W.Z.[Wen-Zhong],
Weakly supervised fine-grained image classification via two-level
attention activation model,
CVIU(218), 2022, pp. 103408.
Elsevier DOI
2205
Fine-grained image classification, Weak supervision, Discriminative region
BibRef
Yan, T.T.[Tian-Tian],
Shi, J.[Jian],
Li, H.J.[Hao-Jie],
Luo, Z.X.[Zhong-Xuan],
Wang, Z.H.[Zhi-Hui],
Discriminative information restoration and extraction for weakly
supervised low-resolution fine-grained image recognition,
PR(127), 2022, pp. 108629.
Elsevier DOI
2205
Low-resolution, Fine-grained image recognition,
Minimum spanning tree, Semantic relation distillation
BibRef
Yu, X.H.[Xiao-Han],
Zhao, Y.[Yang],
Gao, Y.S.[Yong-Sheng],
SPARE: Self-supervised part erasing for ultra-fine-grained visual
categorization,
PR(128), 2022, pp. 108691.
Elsevier DOI
2205
Self-Supervised part erasing,
Ultra-fine-grained visual categorization,
Weakly-supervised part segmentation
BibRef
Liu, H.B.[Hua-Bin],
Li, J.G.[Jian-Guo],
Li, D.[Dian],
See, J.[John],
Lin, W.Y.[Wei-Yao],
Learning Scale-Consistent Attention Part Network for Fine-Grained
Image Recognition,
MultMed(24), 2022, pp. 2902-2913.
IEEE DOI
2206
Image recognition, Task analysis, Logic gates, Location awareness,
Visualization, Training, Object detection,
attention part
BibRef
Tang, H.[Hao],
Yuan, C.C.[Cheng-Cheng],
Li, Z.C.[Ze-Chao],
Tang, J.H.[Jin-Hui],
Learning attention-guided pyramidal features for few-shot
fine-grained recognition,
PR(130), 2022, pp. 108792.
Elsevier DOI
2206
Few-shot learning, Fine-grained recognition, Weakly-supervised learning
BibRef
Deng, W.J.[Wei-Jian],
Marsh, J.[Joshua],
Gould, S.[Stephen],
Zheng, L.[Liang],
Fine-Grained Classification via Categorical Memory Networks,
IP(31), 2022, pp. 4186-4196.
IEEE DOI
2206
Prototypes, Memory modules, Visualization, Semantics,
Representation learning, Convolutional neural networks,
inter-class similarity
BibRef
Yan, T.T.[Tian-Tian],
Li, H.J.[Hao-Jie],
Sun, B.[Baoli],
Wang, Z.H.[Zhi-Hui],
Luo, Z.X.[Zhong-Xuan],
Discriminative Feature Mining and Enhancement Network for
Low-Resolution Fine-Grained Image Recognition,
CirSysVideo(32), No. 8, August 2022, pp. 5319-5330.
IEEE DOI
2208
Feature extraction, Image recognition, Task analysis, Reliability,
Image reconstruction, Automobiles, Training, part selection
BibRef
Zhu, J.W.[Jian-Wei],
Li, Z.X.[Zhi-Xin],
Wei, J.[Jiahui],
Zeng, Y.F.[Yu-Fei],
Ma, H.F.[Hui-Fang],
Fine-grained bidirectional attentional generation and
knowledge-assisted networks for cross-modal retrieval,
IVC(124), 2022, pp. 104507.
Elsevier DOI
2208
Cross-modal retrieval, Graph convolutional network,
Knowledge embedding, Cross-attention, Attentional generative network
BibRef
Peng, J.[Jin],
Wang, Y.X.[Yong-Xiong],
Zhou, Z.[Zeping],
Progressive Erasing Network with consistency loss for fine-grained
visual classification,
JVCIR(87), 2022, pp. 103570.
Elsevier DOI
2208
FGVC, PEN, Multi-grid erasure mechanism,
Cross-layer incentive block, Consistency loss
BibRef
Li, W.S.[Wen-Shu],
Li, S.[Shenhao],
Yin, L.Z.[Ling-Zhi],
Guo, X.Y.[Xiao-Ying],
Yang, X.[Xu],
A novel visual classification framework on panoramic attention
mechanism network,
IET-CV(16), No. 6, 2022, pp. 479-488.
DOI Link
2208
fine-grained image classification model,
multi-branch network for cooperative training, significant feature extraction
BibRef
Lang, W.X.[Wen-Xi],
Sun, H.[Han],
Xu, C.[Can],
Liu, N.Z.[Ning-Zhong],
Zhou, H.Y.[Hui-Yu],
Discriminative feature mining hashing for fine-grained image
retrieval,
JVCIR(87), 2022, pp. 103592.
Elsevier DOI
2208
Fine-grained image retrieval, Attention drop, Attention re-sample, Deep hashing
BibRef
Sun, H.[Han],
Lang, W.X.[Wen-Xi],
Xu, C.[Can],
Liu, N.Z.[Ning-Zhong],
Zhou, H.Y.[Hui-Yu],
Graph-based discriminative features learning for fine-grained image
retrieval,
SP:IC(110), 2023, pp. 116885.
Elsevier DOI
2212
Fine-grained image retrieval, Graph convolutional neural network, Deep hashing
BibRef
Liu, K.J.[Kang-Jun],
Chen, K.[Ke],
Jia, K.[Kui],
Convolutional Fine-Grained Classification With Self-Supervised Target
Relation Regularization,
IP(31), 2022, pp. 5570-5584.
IEEE DOI
2209
Feature extraction, Visualization, Representation learning,
Correlation, Codes, Encoding, Data models,
deep representation learning
BibRef
Yang, X.[Xuhui],
Wang, Y.[Yaowei],
Chen, K.[Ke],
Xu, Y.[Yong],
Tian, Y.H.[Yong-Hong],
Fine-Grained Object Classification via Self-Supervised Pose Alignment,
CVPR22(7389-7398)
IEEE DOI
2210
Representation learning, Codes, Semantics, Benchmark testing,
Image representation, Robustness, Recognition: detection,
Self- semi- meta- unsupervised learning
BibRef
Bera, A.[Asish],
Wharton, Z.[Zachary],
Liu, Y.H.[Yong-Huai],
Bessis, N.[Nik],
Behera, A.[Ardhendu],
SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained
Image Categorization,
IP(31), 2022, pp. 6017-6031.
IEEE DOI
2209
Feature extraction, Visualization, Proposals, Logic gates,
Task analysis, Semantics, Graph neural networks,
relation-aware feature transformation
BibRef
Zhu, Q.X.[Qiang-Xi],
Kuang, W.L.[Wen-Lan],
Li, Z.X.[Zhi-Xin],
Dual attention interactive fine-grained classification network based
on data augmentation,
JVCIR(88), 2022, pp. 103632.
Elsevier DOI
2210
BibRef
And:
Dual-network Multi-attention Collaborative Classification Based on
Fine-grained Vision,
ICPR22(513-520)
IEEE DOI
2212
Data augmentation, Hierarchical training,
Denoising autoencoder, Dual attention mechanism, Interactive attention.
Training, Degradation, Visualization, Atmospheric modeling,
Collaboration, Multilayer perceptrons, Positional attention
BibRef
Cai, Z.H.[Zhen-Huang],
Xie, G.S.[Guo-Sen],
Huang, X.G.[Xing-Guo],
Huang, D.[Dan],
Yao, Y.Z.[Ya-Zhou],
Tang, Z.M.[Zhen-Min],
Robust learning from noisy web data for fine-Grained recognition,
PR(134), 2023, pp. 109063.
Elsevier DOI
2212
Fine-grained, Web-supervised, Noisy web data, Robust learning
BibRef
Sun, Z.[Zeren],
Yao, Y.Z.[Ya-Zhou],
Wei, X.S.[Xiu-Shen],
Shen, F.M.[Fu-Min],
Zhang, J.[Jian],
Hua, X.S.[Xian-Sheng],
Boosting Robust Learning Via Leveraging Reusable Samples in Noisy Web
Data,
MultMed(25), 2023, pp. 3284-3295.
IEEE DOI
2309
BibRef
Zhang, C.Y.[Chuan-Yi],
Wang, Q.[Qiong],
Xie, G.S.[Guo-Sen],
Wu, Q.[Qi],
Shen, F.M.[Fu-Min],
Tang, Z.M.[Zhen-Min],
Robust Learning From Noisy Web Images Via Data Purification for
Fine-Grained Recognition,
MultMed(24), 2022, pp. 1198-1209.
IEEE DOI
2203
Noise measurement, Training, Task analysis, Visualization,
Noise robustness, Labeling, Annotations, Fine-grained, label noise, web images
BibRef
Wei, X.S.[Xiu-Shen],
Song, Y.Z.[Yi-Zhe],
Aodha, O.M.[Oisin Mac],
Wu, J.X.[Jian-Xin],
Peng, Y.X.[Yu-Xin],
Tang, J.H.[Jin-Hui],
Yang, J.[Jian],
Belongie, S.[Serge],
Fine-Grained Image Analysis With Deep Learning: A Survey,
PAMI(44), No. 12, December 2022, pp. 8927-8948.
IEEE DOI
2212
Image recognition, Image analysis, Deep learning, Task analysis,
Image retrieval, Birds, Visualization,
fine-grained image retrieval
BibRef
Zhang, L.[Lianbo],
Huang, S.L.[Shao-Li],
Liu, W.[Wei],
Enhancing Mixture-of-Experts by Leveraging Attention for Fine-Grained
Recognition,
MultMed(24), 2022, pp. 4409-4421.
IEEE DOI
2212
Task analysis, Training, Costs, Data models, Computational modeling,
Location awareness, Image recognition, Deep learning,
data augmentation
BibRef
Song, Y.[Yue],
Sebe, N.[Nicu],
Wang, W.[Wei],
On the Eigenvalues of Global Covariance Pooling for Fine-Grained
Visual Recognition,
PAMI(45), No. 3, March 2023, pp. 3554-3566.
IEEE DOI
2302
Eigenvalues and eigenfunctions, Visualization,
Covariance matrices, Task analysis, Benchmark testing, bilinear pooling
BibRef
Ke, X.[Xiao],
Cai, Y.H.[Yu-Hang],
Chen, B.[Baitao],
Liu, H.[Hao],
Guo, W.Z.[Wen-Zhong],
Granularity-aware distillation and structure modeling region proposal
network for fine-grained image classification,
PR(137), 2023, pp. 109305.
Elsevier DOI
2302
Fine-grained visual classification,
Multi-granularity feature learning, Knowledge distillation, Structure modeling
BibRef
Zhu, Z.[Zining],
Wang, P.[Peijin],
Diao, W.H.[Wen-Hui],
Yang, J.Z.[Jin-Ze],
Wang, H.Q.[Hong-Qi],
Sun, X.[Xian],
Few-shot incremental learning with continual prototype calibration
for remote sensing image fine-grained classification,
PandRS(196), 2023, pp. 210-227.
Elsevier DOI
2302
Incremental learning, Few-shot learning, Remote sensing image,
Fine-grained classification, Continual prototype calibration
BibRef
Ji, R.[Ruyi],
Li, J.Y.[Jia-Ying],
Zhang, L.[Libo],
Siamese self-supervised learning for fine-grained visual
classification,
CVIU(229), 2023, pp. 103658.
Elsevier DOI
2303
Fine-grained visual classification, Seamese network, Self-supervised learning
BibRef
Hu, X.B.[Xia-Bin],
Zhu, S.[Shining],
Peng, T.[Taile],
Hierarchical attention vision transformer for fine-grained visual
classification,
JVCIR(91), 2023, pp. 103755.
Elsevier DOI
2303
Fine-grained visual classification, Vision transformer,
Hierarchical attention selection, Attention-guided data augmentation
BibRef
Zhu, Y.Y.[Ying-Ying],
Cao, G.[Gang],
Yang, Z.Y.[Zhan-Yuan],
Lu, X.F.[Xiu-Fan],
Learning relation-based features for fine-grained image retrieval,
PR(140), 2023, pp. 109543.
Elsevier DOI
2305
Fine-grained image retrieval, Implicit relation, Feature aggregation
BibRef
Wang, Q.[Qi],
Wang, J.J.[Jian-Jun],
Deng, H.Y.[Hong-Yu],
Wu, X.[Xue],
Wang, Y.Z.[Ya-Zhou],
Hao, G.[Gefei],
AA-Trans: Core Attention Aggregating Transformer with Information
Entropy Selector for Fine-Grained Visual Classification,
PR(140), 2023, pp. 109547.
Elsevier DOI
2305
Fine-grained visual, Image classification, Vision transformer,
Attention aggregator, Information entropy
BibRef
Liu, D.[Dichao],
Zhao, L.[Longjiao],
Wang, Y.[Yu],
Kato, J.[Jien],
Learn from each other to Classify better: Cross-layer mutual
attention learning for fine-grained visual classification,
PR(140), 2023, pp. 109550.
Elsevier DOI
2305
Fine-grained recognition, Image classification, Deep features
BibRef
Ye, W.J.[Wu-Jian],
Tan, R.[Run],
Liu, Y.J.[Yi-Jun],
Chang, C.C.[Chin-Chen],
The Comparison of Attention Mechanisms with Different Embedding Modes
for Performance Improvement of Fine-Grained Classification,
IEICE(E106-D), No. 5, May 2023, pp. 590-600.
WWW Link.
2305
BibRef
Wang, M.[Min],
Zhao, P.[Peng],
Lu, X.[Xin],
Min, F.[Fan],
Wang, X.[Xizhao],
Fine-Grained Visual Categorization: A Spatial-Frequency Feature
Fusion Perspective,
CirSysVideo(33), No. 6, June 2023, pp. 2798-2812.
IEEE DOI
2306
Feature extraction, Frequency-domain analysis, Annotations,
Visualization, Labeling, Biological system modeling,
training from scratch
BibRef
Fayou, S.[Sun],
Ngo, H.C.[Hea Choon],
Meng, Z.[Zuqiang],
Sek, Y.W.[Yong Wee],
Loop and distillation: Attention weights fusion transformer for
fine-grained representation,
IET-CV(17), No. 4, 2023, pp. 473-482.
DOI Link
2306
computer vision, fine-grained image recognition, image processing
BibRef
Hsu, Y.C.[Yen-Chi],
Hong, C.Y.[Cheng-Yao],
Lee, M.S.[Ming-Sui],
Geiger, D.[Davi],
Liu, T.L.[Tyng-Luh],
ABC-Norm Regularization for Fine-Grained and Long-Tailed Image
Classification,
IP(32), 2023, pp. 3885-3896.
IEEE DOI
2307
Training, Task analysis, Visualization, Tail, Data models,
Adaptation models, Predictive models, Classification, fine-grained,
regularization
BibRef
Xu, Q.[Qin],
Zhang, M.Q.[Meng-Quan],
Li, Y.[Yun],
Tao, Z.[Zhifu],
Learning more discriminative clues with gradual attention for
fine-grained visual categorization,
IVC(136), 2023, pp. 104753.
Elsevier DOI
2308
Fine-grained visual categorization,
Convolutional neural network, Visual attention, Self-calibrated convolution
BibRef
Robbins, W.[Wes],
Zhou, S.[Steven],
Bhatta, A.[Aman],
Mello, C.[Chad],
Albiero, V.[Vítor],
Bowyer, K.W.[Kevin W.],
Boult, T.E.[Terrance E.],
CAST: Conditional Attribute Subsampling Toolkit for Fine-grained
Evaluation,
WACV23(919-929)
IEEE DOI
2302
Training, Image quality, Deep learning, Face recognition,
Computational modeling, Benchmark testing, ethical computer vision
BibRef
Koch, J.[Jannik],
Wolf, S.[Stefan],
Beyerer, J.[Jürgen],
A Transformer-based Late-Fusion Mechanism for Fine-Grained Object
Recognition in Videos,
RealWorld23(1-10)
IEEE DOI
2302
Costs, Surveillance, Streaming media, Transformers,
Real-time systems, Consensus protocol
BibRef
Park, H.[Hojin],
Park, J.[Jaewoo],
Teoh, A.B.J.[Andrew Beng Jin],
Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning,
ICPR22(1026-1032)
IEEE DOI
2212
WWW Link. Face recognition, Source coding, Computational modeling,
Benchmark testing, Task analysis
BibRef
Li, M.X.[Meng-Xuan],
Liu, Y.[Yan],
Liu, Q.[Qi],
Chen, S.L.[Song-Lu],
Chen, F.[Feng],
Yin, X.C.[Xu-Cheng],
Semi-Supervised Fine-Grained Classification with Web Data via Noisy
Sample Selection,
ICPR22(5024-5030)
IEEE DOI
2212
Training, Benchmark testing, Robustness, Data models,
Noise measurement
BibRef
Liu, Y.[Yang],
Zhou, L.[Lei],
Zhang, P.C.[Peng-Cheng],
Bai, X.[Xiao],
Gu, L.[Lin],
Yu, X.H.[Xiao-Han],
Zhou, J.[Jun],
Hancock, E.R.[Edwin R.],
Where to Focus: Investigating Hierarchical Attention Relationship for
Fine-Grained Visual Classification,
ECCV22(XXIV:57-73).
Springer DOI
2211
BibRef
Shu, Y.Y.[Yang-Yang],
Yu, B.[Baosheng],
Xu, H.[Haiming],
Liu, L.Q.[Ling-Qiao],
Improving Fine-Grained Visual Recognition in Low Data Regimes via
Self-boosting Attention Mechanism,
ECCV22(XXV:449-465).
Springer DOI
2211
BibRef
Wang, B.S.[Bor-Shiun],
Hsieh, J.W.[Jun-Wei],
Hsieh, Y.K.[Yi-Kuan],
Chen, P.Y.[Ping-Yang],
COFENet:
Co-Feature Neural Network Model for Fine-Grained Image Classification,
ICIP22(3928-3932)
IEEE DOI
2211
Deep learning, Analytical models, Neural networks, Merging, Layout,
Feature extraction, Image classification, Texture classification,
Fine-grained classification
BibRef
Kang, B.[Bin],
Wu, F.[Fan],
Li, X.[Xin],
Zhou, Q.[Quan],
Progressive Training Enabled Fine-Grained Recognition,
ICIP22(876-880)
IEEE DOI
2211
Training, Learning systems, Image recognition, Dogs, Optimization,
Convergence, Fine-grained recognition, Submodular optimization, Group ranking
BibRef
Thomas, C.[Christopher],
Zhang, Y.P.[Yi-Peng],
Chang, S.F.[Shih-Fu],
Fine-Grained Visual Entailment,
ECCV22(XXXVI:398-416).
Springer DOI
2211
BibRef
Yang, L.F.[Ling-Feng],
Li, X.[Xiang],
Song, R.J.[Ren-Jie],
Zhao, B.[Borui],
Tao, J.[Juntian],
Zhou, S.H.[Shi-Hao],
Liang, J.J.[Jia-Jun],
Yang, J.[Jian],
Dynamic MLP for Fine-Grained Image Classification by Leveraging
Geographical and Temporal Information,
CVPR22(10935-10944)
IEEE DOI
2210
Knowledge engineering, Visualization, Image recognition, Fuses,
Heuristic algorithms, Image representation, retrieval
BibRef
Xu, H.[Hang],
Kang, N.[Ning],
Zhang, G.[Gengwei],
Xie, C.L.[Chuan-Long],
Liang, X.D.[Xiao-Dan],
Li, Z.G.[Zhen-Guo],
NASOA:
Towards Faster Task-Oriented Online Fine-Tuning with a Zoo of Models,
ICCV21(5077-5086)
IEEE DOI
2203
Training, Adaptation models, Cloud computing, Schedules,
Computational modeling, Graphics processing units,
Vision applications and systems
BibRef
Touvron, H.[Hugo],
Sablayrolles, A.[Alexandre],
Douze, M.[Matthijs],
Cord, M.[Matthieu],
Jégou, H.[Hervé],
Grafit: Learning fine-grained image representations with coarse
labels,
ICCV21(854-864)
IEEE DOI
2203
Training, Annotations, Transfer learning, Neural networks,
Image representation, Task analysis, Representation learning
BibRef
Huang, S.L.[Shao-Li],
Wang, X.C.[Xin-Chao],
Tao, D.C.[Da-Cheng],
Stochastic Partial Swap: Enhanced Model Generalization and
Interpretability for Fine-grained Recognition,
ICCV21(600-609)
IEEE DOI
2203
Training, Representation learning, Image recognition,
Computational modeling, Neurons, Stochastic processes,
Representation learning
BibRef
Joung, S.[Sunghun],
Kim, S.[Seungryong],
Kim, M.[Minsu],
Kim, I.J.[Ig-Jae],
Sohn, K.H.[Kwang-Hoon],
Learning Canonical 3D Object Representation for Fine-Grained
Recognition,
ICCV21(1015-1025)
IEEE DOI
2203
Representation learning, Solid modeling, Shape, Annotations, Cameras,
Object recognition, Recognition and classification,
Representation learning
BibRef
Kim, J.[Junho],
Bae, J.[Jaehyeok],
Park, G.[Gangin],
Zhang, D.[Dongsu],
Kim, Y.M.[Young Min],
N-ImageNet: Towards Robust, Fine-Grained Object Recognition with
Event Cameras,
ICCV21(2126-2136)
IEEE DOI
2203
Degradation, Lighting, Benchmark testing, Cameras, Robustness,
Hardware, Classification algorithms, Computational photography,
Datasets and evaluation
BibRef
Yu, X.H.[Xiao-Han],
Zhao, Y.[Yang],
Gao, Y.S.[Yong-Sheng],
Yuan, X.H.[Xiao-Hui],
Xiong, S.W.[Sheng-Wu],
Benchmark Platform for Ultra-Fine-Grained Visual Categorization
Beyond Human Performance,
ICCV21(10265-10275)
IEEE DOI
2203
Deep learning, Training, Visualization, Protocols, Annotations,
Benchmark testing, Datasets and evaluation, Medical, biological,
Recognition and classification
BibRef
Sun, Z.[Zeren],
Yao, Y.Z.[Ya-Zhou],
Wei, X.S.[Xiu-Shen],
Zhang, Y.S.[Yong-Shun],
Shen, F.M.[Fu-Min],
Wu, J.X.[Jian-Xin],
Zhang, J.[Jian],
Shen, H.T.[Heng Tao],
Webly Supervised Fine-Grained Recognition:
Benchmark Datasets and an Approach,
ICCV21(10582-10591)
IEEE DOI
2203
Training, Deep learning, Knowledge engineering, Image recognition,
Target recognition, Neural networks, Datasets and evaluation,
BibRef
ipka, T.[Tomá],
ulc, M.[Milan],
Matas, J.G.[Jirí G.],
The Hitchhiker's Guide to Prior-Shift Adaptation,
WACV22(2031-2039)
IEEE DOI
2202
Maximum a posteriori estimation, Training,
Maximum likelihood estimation, Image recognition,
Object Detection/Recognition/Categorization
BibRef
Xu, Z.R.[Zi-Rui],
Yu, F.X.[Fu-Xun],
Liu, C.X.[Chen-Xi],
Wu, Z.[Zhe],
Wang, H.C.[Hong-Cheng],
Chen, X.[Xiang],
FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed
Convolutions for Inference Optimization,
WACV22(3634-3644)
IEEE DOI
2202
Adaptation models, Convolution, Shape,
Computational modeling, Optimization, Pattern matching,
Deep Learning -> Efficient Training and Inference Methods for
Networks Deep Learning
BibRef
Mahmoudi, M.A.[M. Amine],
Chetouani, A.[Aladine],
Boufera, F.[Fatma],
Tabia, H.[Hedi],
Taylor Series Kernelized Layer for Fine-Grained Recognition,
ICIP21(1914-1918)
IEEE DOI
2201
Image recognition, Multilayer perceptrons, Taylor series,
Hilbert space, Convolutional neural networks, Kernel,
Multilayer Perceptrons
BibRef
Tian, S.[Sheng],
Tang, H.[Hao],
Dai, L.Q.[Long-Quan],
Coupled Patch Similarity Network FOR One-Shot Fine-Grained Image
Recognition,
ICIP21(2478-2482)
IEEE DOI
2201
Measurement, Training, Image recognition, Benchmark testing,
Feature extraction, Generators, one-shot, fine-grained, image recognition
BibRef
Wang, J.[Jun],
Yu, X.H.[Xiao-Han],
Gao, Y.S.[Yong-Sheng],
Mask Guided Attention for Fine-Grained Patchy Image Classification,
ICIP21(1044-1048)
IEEE DOI
2201
Training, Image segmentation, Semantics, Training data, Robustness,
Image classification, Mask, attention, semantic segmentation,
fine-grained patchy image classification
BibRef
Sun, Y.J.[Ya-Jie],
Zhang, M.H.[Miao-Hua],
Yu, X.H.[Xiao-Han],
Liao, Y.[Yi],
Gao, Y.S.[Yong-Sheng],
A Compositional Feature Embedding and Similarity Metric for
Ultra-Fine-Grained Visual Categorization,
DICTA21(01-08)
IEEE DOI
2201
Measurement, Training, Visualization, Digital images,
Computational modeling, Training data, Benchmark testing, CottonCultivar
BibRef
Pan, Z.C.[Zi-Cheng],
Yu, X.H.[Xiao-Han],
Zhang, M.[Miaohua],
Gao, Y.S.[Yong-Sheng],
Mask-Guided Feature Extraction and Augmentation for
Ultra-Fine-Grained Visual Categorization,
DICTA21(1-8)
IEEE DOI
2201
Even finer-grained.
Training, Visualization, Annotations, Feature detection,
Digital images, Benchmark testing, Feature extraction,
attention
BibRef
Fan, L.[Luyu],
Wang, Q.[Qi],
Wang, Y.B.[Yong-Bin],
Long-Range Comprehensive Modeling for Fine-Grained Visual
Classification,
ICIVC21(196-201)
IEEE DOI
2112
Visualization, Fuses, Semantics, Neural networks, Object detection,
Feature extraction, Transformers, deep learning,
transformer
BibRef
Achille, A.[Alessandro],
Golatkar, A.[Aditya],
Ravichandran, A.[Avinash],
Polito, M.[Marzia],
Soatto, S.[Stefano],
LQF: Linear Quadratic Fine-Tuning,
CVPR21(15724-15734)
IEEE DOI
2111
Deep learning, Training data,
Computer architecture, Robustness, Pattern recognition, Task analysis
BibRef
Yan, S.Y.[Shi-Yang],
Yu, L.[Li],
Xie, Y.[Yuan],
Discrete-continuous Action Space Policy Gradient-based Attention for
Image-Text Matching,
CVPR21(8092-8101)
IEEE DOI
2111
Measurement, Annotations, Computational modeling,
Semantics, Transforms, Benchmark testing
BibRef
Nauta, M.[Meike],
van Bree, R.[Ron],
Seifert, C.[Christin],
Neural Prototype Trees for Interpretable Fine-grained Image
Recognition,
CVPR21(14928-14938)
IEEE DOI
2111
Deep learning, Training, Image recognition, Neural networks,
Prototypes, Predictive models, Birds
BibRef
Zhao, Y.F.[Yi-Fan],
Yan, K.[Ke],
Huang, F.Y.[Fei-Yue],
Li, J.[Jia],
Graph-based High-Order Relation Discovery for Fine-grained
Recognition,
CVPR21(15074-15083)
IEEE DOI
2111
Training, Manifolds, Tensors, Semantics, Focusing, Collaborative work
BibRef
Chang, D.L.[Dong-Liang],
Pang, K.Y.[Kai-Yue],
Zheng, Y.X.[Yi-Xiao],
Ma, Z.Y.[Zhan-Yu],
Song, Y.Z.[Yi-Zhe],
Guo, J.[Jun],
Your 'Flamingo' is My 'Bird': Fine-Grained, or Not,
CVPR21(11471-11480)
IEEE DOI
2111
Visualization, Codes, Birds, Pattern recognition, Information exchange
BibRef
Su, J.C.[Jong-Chyi],
Cheng, Z.[Zezhou],
Maji, S.[Subhransu],
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained
Classification,
CVPR21(12961-12970)
IEEE DOI
2111
Fungi, Technological innovation, Transfer learning, Taxonomy,
Benchmark testing, Semisupervised learning, Pattern recognition
BibRef
Xu, F.[Furong],
Wang, M.[Meng],
Zhang, W.[Wei],
Cheng, Y.[Yuan],
Chu, W.[Wei],
Discrimination-Aware Mechanism for Fine-grained Representation
Learning,
CVPR21(813-822)
IEEE DOI
2111
Training, Industries, Visualization, Dams,
Feature extraction, Birds
BibRef
Cheng, J.C.[Jia-Cheng],
Vasconcelos, N.M.[Nuno M.],
Learning Deep Classifiers Consistent with Fine-Grained Novelty
Detection,
CVPR21(1664-1673)
IEEE DOI
2111
Measurement, Training, Visualization,
Probabilistic logic, Pattern recognition, Convolutional neural networks
BibRef
Bukchin, G.[Guy],
Schwartz, E.[Eli],
Saenko, K.[Kate],
Shahar, O.[Ori],
Feris, R.[Rogerio],
Giryes, R.[Raja],
Karlinsky, L.[Leonid],
Fine-grained Angular Contrastive Learning with Coarse Labels,
CVPR21(8726-8736)
IEEE DOI
2111
Training, Learning systems, Adaptation models,
Animals, Pattern recognition, Task analysis
BibRef
Mafla, A.[Andres],
Dey, S.[Sounak],
Biten, A.F.[Ali Furkan],
Gomez, L.[Lluis],
Karatzas, D.[Dimosthenis],
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image
Classification and Retrieval,
WACV21(4022-4032)
IEEE DOI
2106
Visualization, Semantics, Pipelines, Image retrieval, Cognition
BibRef
Zhang, L.B.[Lian-Bo],
Huang, S.L.[Shao-Li],
Liu, W.[Wei],
Intra-class Part Swapping for Fine-Grained Image Classification,
WACV21(3208-3217)
IEEE DOI
2106
Location awareness, Training, Image recognition,
Training data, Data models, Noise measurement
BibRef
Gwilliam, M.[Matthew],
Teuscher, A.[Adam],
Anderson, C.[Connor],
Farrell, R.[Ryan],
Fair Comparison: Quantifying Variance in Results for Fine-grained
Visual Categorization,
WACV21(3308-3317)
IEEE DOI
2106
Measurement, Visualization,
Computer architecture, Data models, Task analysis
BibRef
Zhang, F.[Fan],
Li, M.[Meng],
Zhai, G.S.[Gui-Sheng],
Liu, Y.Z.[Yi-Zhao],
Multi-branch and Multi-scale Attention Learning for Fine-grained Visual
Categorization,
MMMod21(I:136-147).
Springer DOI
2106
BibRef
Shi, X.W.[Xiang-Wei],
Khademi, S.[Seyran],
Li, Y.Q.[Yun-Qiang],
van Gemert, J.C.[Jan C.],
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels,
ICPR21(10289-10296)
IEEE DOI
2105
Location awareness, Training, Visualization, Image segmentation,
Image resolution, Computational modeling, Semantics
BibRef
Wang, Q.T.[Qing-Tao],
Zhang, K.[Ke],
Fan, J.[Jin],
Huang, S.L.[Shao-Li],
Zhang, L.B.[Lian-Bo],
Multi-Order Feature Statistical Model for Fine-Grained Visual
Categorization,
ICPR21(7379-7386)
IEEE DOI
2105
Visualization, Statistical analysis, Image representation,
Benchmark testing, Feature extraction, feature learning
BibRef
Yuan, L.X.[Li-Xian],
Chen, R.Q.[Ri-Quan],
Wu, H.F.[He-Feng],
Chen, T.S.[Tian-Shui],
Wang, W.T.[Wen-Tao],
Chen, P.[Pei],
Exploiting Knowledge Embedded Soft Labels for Image Recognition,
ICPR21(4989-4995)
IEEE DOI
2105
Knowledge engineering, Training, Correlation, Image recognition,
Semantics, Benchmark testing, Birds
BibRef
Yuan, H.[Hui],
Huang, Y.[Yan],
Zhang, D.B.[Dong-Bo],
Chen, Z.[Zerui],
Cheng, W.L.[Wen-Long],
Wang, L.[Liang],
VSR++: Improving Visual Semantic Reasoning for Fine-Grained
Image-Text Matching,
ICPR21(3728-3735)
IEEE DOI
2105
Visualization, Semantics, Benchmark testing, Cognition, Pattern matching
BibRef
Lin, H.L.[Hong-Li],
Song, Y.Q.[Yong-Qi],
Zeng, Z.X.[Zi-Xuan],
Wang, W.S.[Wei-Sheng],
Wang, J.Y.[Jia-Yi],
Aggregating Object Features Based on Attention Weights for
Fine-Grained Image Retrieval,
ICPR21(2838-2844)
IEEE DOI
2105
Location awareness, Weight measurement, Image retrieval,
Feature extraction
BibRef
Zhang, H.[He],
Bai, Y.M.[Yun-Ming],
Zhang, H.[Hui],
Liu, J.[Jing],
Li, X.G.[Xing-Guang],
He, Z.F.[Zhao-Feng],
Local Attention and Global Representation Collaborating for
Fine-grained Classification,
ICPR21(10658-10665)
IEEE DOI
2105
Location awareness, Image quality, Annotations, Manuals,
Object detection, Pattern recognition, Task analysis
BibRef
Mugnai, D.[Daniele],
Pernici, F.[Federico],
Turchini, F.[Francesco],
del Bimbo, A.[Alberto],
Soft Pseudo-labeling Semi-supervised Learning Applied to Fine-grained
Visual Classification,
MLCSA20(102-110).
Springer DOI
2103
BibRef
Nakka, K.K.[Krishna Kanth],
Salzmann, M.[Mathieu],
Towards Robust Fine-grained Recognition by Maximal Separation of
Discriminative Features,
ACCV20(VI:391-408).
Springer DOI
2103
BibRef
Protopapadakis, E.[Eftychios],
Doulamis, A.[Anastasios],
Doulamis, N.[Nikolaos],
Maltezos, E.[Evangelos],
Semi-supervised Fine-tuning for Deep Learning Models in Remote Sensing
Applications,
ISVC20(I:719-730).
Springer DOI
2103
BibRef
Imran, A.[Ashiq],
Athitsos, V.[Vassilis],
Adaptive Feature Norm for Unsupervised Subdomain Adaptation,
ISVC21(I:341-352).
Springer DOI
2112
BibRef
Imran, A.[Ashiq],
Athitsos, V.[Vassilis],
Domain Adaptive Transfer Learning on Visual Attention Aware Data
Augmentation for Fine-grained Visual Categorization,
ISVC20(II:53-65).
Springer DOI
2103
BibRef
Li, H.,
Zhang, X.,
Tian, Q.,
Xiong, H.,
Attribute Mix: Semantic Data Augmentation for Fine Grained
Recognition,
VCIP20(243-246)
IEEE DOI
2102
data mining, image classification, image representation,
learning (artificial intelligence), object recognition,
Attribute Augmentation
BibRef
Shen, Y.[Yang],
Sun, X.[Xuhao],
Wei, X.S.[Xiu-Shen],
Jiang, Q.Y.[Qing-Yuan],
Yang, J.[Jian],
SEMICON: A Learning-to-Hash Solution for Large-Scale Fine-Grained Image
Retrieval,
ECCV22(XIV:531-548).
Springer DOI
2211
BibRef
Cui, Q.[Quan],
Jiang, Q.Y.[Qing-Yuan],
Wei, X.S.[Xiu-Shen],
Li, W.J.[Wu-Jun],
Yoshie, O.[Osamu],
Exchnet: A Unified Hashing Network for Large-scale Fine-grained Image
Retrieval,
ECCV20(III:189-205).
Springer DOI
2012
BibRef
Ju, M.,
Ryu, H.,
Moon, S.,
Yoo, C.D.,
GAPNet: Generic-Attribute-Pose Network For Fine-Grained Visual
Categorization Using Multi-Attribute Attention Module,
ICIP20(703-707)
IEEE DOI
2011
Feature extraction, Birds, Visualization, Streaming media,
Image color analysis, Task analysis, Indexes,
Attention Mechanism
BibRef
Ha, M.L.,
Blanz, V.,
Neural Discriminant Analysis For Fine-Grained Classification,
ICIP20(1656-1660)
IEEE DOI
2011
Optimization, Neural networks, Feature extraction, Training,
Visualization, Birds, Linear discriminant analysis, LDA, NDA,
fine-grained classification
BibRef
Jia, S.,
Bai, Y.,
Zhang, J.,
Feature Comparison Based Channel Attention For Fine-Grained Visual
Classification,
ICIP20(1776-1780)
IEEE DOI
2011
Visualization, Feature extraction, Training, Atmospheric modeling,
Automobiles, Data models, Aircraft, Channel attention,
fine-grained visual classification
BibRef
Ye, Z.,
Hu, F.,
Liu, Y.,
Xia, Z.,
Lyu, F.,
Liu, P.,
Associating Multi-Scale Receptive Fields For Fine-Grained Recognition,
ICIP20(1851-1855)
IEEE DOI
2011
Feature extraction, Computational modeling, Dogs, Training,
Automobiles, Neurons, Image recognition, Deep learning,
receptive field.
BibRef
Achlioptas, P.[Panos],
Abdelreheem, A.[Ahmed],
Xia, F.[Fei],
Elhoseiny, M.[Mohamed],
Guibas, L.J.[Leonidas J.],
Referit3d: Neural Listeners for Fine-grained 3d Object Identification
in Real-world Scenes,
ECCV20(I:422-440).
Springer DOI
2011
BibRef
Abdelreheem, A.[Ahmed],
Upadhyay, U.[Ujjwal],
Skorokhodov, I.[Ivan],
Al Yahya, R.[Rawan],
Chen, J.[Jun],
Elhoseiny, M.[Mohamed],
3DRefTransformer: Fine-Grained Object Identification in Real-World
Scenes Using Natural Language,
WACV22(607-616)
IEEE DOI
2202
Training, Point cloud compression, Visualization, Solid modeling,
Neural networks, Transformers,
Analysis and Understanding
BibRef
Shroff, P.,
Chen, T.,
Wei, Y.,
Wang, Z.,
Focus Longer to See Better: Recursively Refined Attention for
Fine-Grained Image Classification,
UG20(3791-3798)
IEEE DOI
2008
Feature extraction, Streaming media, Neural networks,
Visualization, Task analysis, Aggregates, Image recognition
BibRef
Desai, S.V.[S. Vikas],
Balasubramanian, V.N.,
Towards Fine-grained Sampling for Active Learning in Object Detection,
VL3W20(4010-4014)
IEEE DOI
2008
Object detection, Training, Labeling, Measurement, Uncertainty,
Data models, Detectors
BibRef
Huang, Z.,
Li, Y.,
Interpretable and Accurate Fine-grained Recognition via Region
Grouping,
CVPR20(8659-8669)
IEEE DOI
2008
Birds, Visualization, Image segmentation, Dictionaries,
Pattern recognition, Object recognition
BibRef
Ji, R.,
Wen, L.,
Zhang, L.,
Du, D.,
Wu, Y.,
Zhao, C.,
Liu, X.,
Huang, F.,
Attention Convolutional Binary Neural Tree for Fine-Grained Visual
Categorization,
CVPR20(10465-10474)
IEEE DOI
2008
Vegetation, Routing, Visualization, Task analysis, Decision trees,
Convolutional codes, Binary trees
BibRef
Wang, Z.,
Wang, S.,
Yang, S.,
Li, H.,
Li, J.,
Li, Z.,
Weakly Supervised Fine-Grained Image Classification via Guassian
Mixture Model Oriented Discriminative Learning,
CVPR20(9746-9755)
IEEE DOI
2008
Gaussian mixture model, Correlation, Feature extraction, Kernel,
Image recognition, Semantics
BibRef
Ha, M.L.,
Hosu, V.,
Blanz, V.,
Color Composition Similarity and Its Application in Fine-grained
Similarity,
WACV20(2548-2557)
IEEE DOI
2006
Image color analysis, Visualization, Measurement,
Feature extraction, Layout, Training data, Predictive models
BibRef
Mafla, A.,
Dey, S.,
Biten, A.F.,
Gomez, L.,
Karatzas, D.,
Fine-grained Image Classification and Retrieval by Combining Visual
and Locally Pooled Textual Features,
WACV20(2939-2948)
IEEE DOI
2006
Visualization, Feature extraction, Task analysis, Text recognition,
Semantics, Image retrieval
BibRef
Hanselmann, H.,
Ney, H.,
ELoPE: Fine-Grained Visual Classification with Efficient
Localization, Pooling and Embedding,
WACV20(1236-1245)
IEEE DOI
2006
Training, Task analysis, Automobiles, Standards, Visualization, Birds, Testing
BibRef
Basirat, M.,
Roth, P.M.,
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained
Visual Categorization,
WACV20(1207-1216)
IEEE DOI
2006
Task analysis, Visualization, Robustness, Training,
Computational modeling, Convergence, Machine learning
BibRef
Taherkhani, F.,
Kazemi, H.,
Dabouei, A.,
Dawson, J.,
Nasrabadi, N.,
A Weakly Supervised Fine Label Classifier Enhanced by Coarse
Supervision,
ICCV19(6458-6467)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, image enhancement, image representation
BibRef
Azimi, S.M.,
Henry, C.,
Sommer, L.,
Schumann, A.,
Vig, E.,
SkyScapes: Fine-Grained Semantic Understanding of Aerial Scenes,
ICCV19(7392-7402)
IEEE DOI
2004
edge detection, feature extraction, image capture,
image segmentation, town and country planning, aerial scenes,
Vegetation
BibRef
Wei, K.,
Yang, M.,
Wang, H.,
Deng, C.,
Liu, X.,
Adversarial Fine-Grained Composition Learning for Unseen
Attribute-Object Recognition,
ICCV19(3740-3748)
IEEE DOI
2004
feature extraction, image classification,
learning (artificial intelligence), object recognition, Cats
BibRef
Luo, W.,
Yang, X.,
Mo, X.,
Lu, Y.,
Davis, L.,
Li, J.,
Yang, J.,
Lim, S.,
Cross-X Learning for Fine-Grained Visual Categorization,
ICCV19(8241-8250)
IEEE DOI
2004
Code, Learning.
WWW Link. feature extraction, image classification, image representation,
neural nets, object recognition, supervised learning,
Visualization
BibRef
Zhang, L.,
Huang, S.,
Liu, W.,
Tao, D.,
Learning a Mixture of Granularity-Specific Experts for Fine-Grained
Categorization,
ICCV19(8330-8339)
IEEE DOI
2004
image classification, learning (artificial intelligence),
granularity-specific experts, fine-grained categorization,
Artificial intelligence
BibRef
Aodha, O.M.,
Cole, E.,
Perona, P.,
Presence-Only Geographical Priors for Fine-Grained Image
Classification,
ICCV19(9595-9605)
IEEE DOI
2004
image classification, image segmentation,
learning (artificial intelligence), object detection,
Standards
BibRef
Yang, H.,
Wu, H.,
Chen, H.,
Detecting 11K Classes: Large Scale Object Detection Without
Fine-Grained Bounding Boxes,
ICCV19(9804-9812)
IEEE DOI
2004
convolutional neural nets, image annotation,
learning (artificial intelligence), object detection,
Training
BibRef
Wang, H.,
Saligrama, V.,
Sclaroff, S.,
Ablavsky, V.,
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential
Fixations,
ICCV19(1252-1261)
IEEE DOI
2004
image classification, Internet of Things,
learning (artificial intelligence), neural nets, Computational modeling
BibRef
Chu, G.,
Potetz, B.,
Wang, W.,
Howard, A.,
Song, Y.,
Brucher, F.,
Leung, T.,
Adam, H.,
Geo-Aware Networks for Fine-Grained Recognition,
CVWC19(247-254)
IEEE DOI
2004
image classification, image recognition, mobile computing,
geo-aware networks, subtle visual differences, image classification
BibRef
Liao, Q.,
Wang, D.,
Holewa, H.,
Xu, M.,
Squeezed Bilinear Pooling for Fine-Grained Visual Categorization,
SDL-CV19(728-732)
IEEE DOI
2004
feature extraction, image classification,
highly semantic feature channel extraction, Computational modeling
BibRef
Wagner, J.[Jorg],
Kohler, J.M.[Jan Mathias],
Gindele, T.[Tobias],
Hetzel, L.[Leon],
Wiedemer, J.T.[Jakob Thaddaus],
Behnke, S.[Sven],
Interpretable and Fine-Grained Visual Explanations for Convolutional
Neural Networks,
CVPR19(9089-9099).
IEEE DOI
2002
BibRef
Yu, F.G.[Feng-Gen],
Liu, K.[Kun],
Zhang, Y.[Yan],
Zhu, C.Y.[Chen-Yang],
Xu, K.[Kai],
PartNet: A Recursive Part Decomposition Network for Fine-Grained and
Hierarchical Shape Segmentation,
CVPR19(9483-9492).
IEEE DOI
2002
BibRef
Singh, K.K.[Krishna Kumar],
Ojha, U.[Utkarsh],
Lee, Y.J.[Yong Jae],
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained
Object Generation and Discovery,
CVPR19(6483-6492).
IEEE DOI
2002
BibRef
Chen, Y.[Yue],
Bai, Y.[Yalong],
Zhang, W.[Wei],
Mei, T.[Tao],
Destruction and Construction Learning for Fine-Grained Image
Recognition,
CVPR19(5152-5161).
IEEE DOI
2002
BibRef
Zheng, H.L.[He-Liang],
Fu, J.L.[Jian-Long],
Zha, Z.J.[Zheng-Jun],
Luo, J.B.[Jie-Bo],
Looking for the Devil in the Details: Learning Trilinear Attention
Sampling Network for Fine-Grained Image Recognition,
CVPR19(5007-5016).
IEEE DOI
2002
BibRef
Ge, W.F.[Wei-Feng],
Lin, X.R.[Xiang-Ru],
Yu, Y.[Yizhou],
Weakly Supervised Complementary Parts Models for Fine-Grained Image
Classification From the Bottom Up,
CVPR19(3029-3038).
IEEE DOI
2002
BibRef
Mo, K.[Kaichun],
Zhu, S.L.[Shi-Lin],
Chang, A.X.[Angel X.],
Yi, L.[Li],
Tripathi, S.[Subarna],
Guibas, L.J.[Leonidas J.],
Su, H.[Hao],
PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical
Part-Level 3D Object Understanding,
CVPR19(909-918).
IEEE DOI
2002
BibRef
Brust, C.A.[Clemens-Alexander],
Denzler, J.[Joachim],
Not Just a Matter of Semantics:
The Relationship Between Visual and Semantic Similarity,
GCPR19(414-427).
Springer DOI
1911
BibRef
Korsch, D.[Dimitri],
Bodesheim, P.[Paul],
Denzler, J.[Joachim],
Classification-Specific Parts for Improving Fine-Grained Visual
Categorization,
GCPR19(62-75).
Springer DOI
1911
BibRef
Karaman, K.,
Gundogdu, E.,
Koç, A.,
Alatan, A.A.,
Quadruplet Selection Methods for Deep Embedding Learning,
ICIP19(3452-3456)
IEEE DOI
1910
Deep distance metric learning, embedding learning,
fine-grained classification/recognition
BibRef
Feng, Z.,
Fu, K.,
Zhao, Q.,
Learning to Focus and Discriminate for Fine-Grained Classification,
ICIP19(415-419)
IEEE DOI
1910
Fine-grained classification, region proposal,
discriminative region localization, attention, convolutional neural networks
BibRef
Xin, Q.,
Lv, T.,
Gao, H.,
Random Part Localization Model for Fine Grained Image Classification,
ICIP19(420-424)
IEEE DOI
1910
fine-grained, convolutional neural network, random part localization
BibRef
Guo, P.,
Farrell, R.,
Aligned to the Object, Not to the Image: A Unified Pose-Aligned
Representation for Fine-Grained Recognition,
WACV19(1876-1885)
IEEE DOI
1904
feature extraction, image classification,
image colour analysis, image representation,
Image recognition
BibRef
Lu, C.H.[Chao-Hao],
Zou, Y.X.[Yue-Xian],
Using Coarse Label Constraint for Fine-Grained Visual Classification,
MMMod19(II:266-277).
Springer DOI
1901
BibRef
Zhou, Y.,
Shen, S.,
Hu, Z.,
Fine-Level Semantic Labeling of Large-Scale 3D Model by Active
Learning,
3DV18(523-532)
IEEE DOI
1812
computational geometry, feedforward neural nets,
image resolution, image segmentation, iterative methods,
Large Scale
BibRef
Lin, J.,
Lin, Y.,
King, E.,
Su, H.,
Hsu, W.H.,
Cross-Domain Hallucination Network for Fine-Grained Object
Recognition,
PBVS18(1295-12957)
IEEE DOI
1812
Surveillance, Image resolution, Feature extraction,
Object recognition, Image recognition, Image reconstruction,
fine-grained classification
BibRef
Zhong, W.,
Jiang, L.,
Zhang, T.,
Ji, J.,
Xiong, H.,
A Multi-part Convolutional Attention Network for Fine-Grained Image
Recognition,
ICPR18(1857-1862)
IEEE DOI
1812
Object detection, Feature extraction, Streaming media,
Image recognition, Image resolution, Task analysis, Automobiles
BibRef
Wang, Y.,
Morariu, V.I.,
Davis, L.S.,
Learning a Discriminative Filter Bank Within a CNN for Fine-Grained
Recognition,
CVPR18(4148-4157)
IEEE DOI
1812
Detectors, Encoding, Convolutional codes, Neurons,
Feature extraction, Network architecture, Convolution
BibRef
Fan, L.[Lei],
Ding, Y.[Yiwen],
Fan, D.D.[Dong-Dong],
Di, D.L.[Dong-Lin],
Pagnucco, M.[Maurice],
Song, Y.[Yang],
GrainSpace: A Large-scale Dataset for Fine-grained and
Domain-adaptive Recognition of Cereal Grains,
CVPR22(21084-21093)
IEEE DOI
2210
International trade, Hand tools, Data acquisition, Prototypes,
Self-supervised learning, Inspection, Semisupervised learning,
Vision applications and systems
BibRef
Cui, Y.[Yin],
Song, Y.[Yang],
Sun, C.[Chen],
Howard, A.[Andrew],
Belongie, S.[Serge],
Large Scale Fine-Grained Categorization and Domain-Specific Transfer
Learning,
CVPR18(4109-4118)
IEEE DOI
1812
Training, Visualization, Feature extraction, Image resolution,
Training data, Task analysis, Image recognition
BibRef
Niu, L.,
Veeraraghavan, A.,
Sabharwal, A.,
Webly Supervised Learning Meets Zero-shot Learning:
A Hybrid Approach for Fine-Grained Classification,
CVPR18(7171-7180)
IEEE DOI
1812
Training, Semantics, Noise measurement, Supervised learning,
Visualization, Knowledge engineering, Birds
BibRef
Zhang, Y.B.[Ya-Bin],
Tang, H.[Hui],
Jia, K.[Kui],
Fine-Grained Visual Categorization Using Meta-learning Optimization
with Sample Selection of Auxiliary Data,
ECCV18(VIII: 241-256).
Springer DOI
1810
BibRef
Rodríguez, P.[Pau],
Gonfaus, J.M.[Josep M.],
Cucurull, G.[Guillem],
Roca, F.X.[F. Xavier],
Gonzŕlez, J.[Jordi],
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained
Recovery,
ECCV18(VIII: 357-372).
Springer DOI
1810
BibRef
Yu, C.J.[Chao-Jian],
Zhao, X.Y.[Xin-Yi],
Zheng, Q.[Qi],
Zhang, P.[Peng],
You, X.G.[Xin-Ge],
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition,
ECCV18(XVI: 595-610).
Springer DOI
1810
BibRef
Dubey, A.[Abhimanyu],
Gupta, O.[Otkrist],
Guo, P.[Pei],
Raskar, R.[Ramesh],
Farrell, R.[Ryan],
Naik, N.[Nikhil],
Pairwise Confusion for Fine-Grained Visual Classification,
ECCV18(XII: 71-88).
Springer DOI
1810
BibRef
Zhu, C.[Chen],
Tan, X.[Xiao],
Zhou, F.[Feng],
Liu, X.[Xiao],
Yue, K.Y.[Kai-Yu],
Ding, E.[Errui],
Ma, Y.[Yi],
Fine-Grained Video Categorization with Redundancy Reduction Attention,
ECCV18(VI: 139-155).
Springer DOI
1810
BibRef
Wei, X.[Xing],
Zhang, Y.[Yue],
Gong, Y.H.[Yi-Hong],
Zhang, J.W.[Jia-Wei],
Zheng, N.N.[Nan-Ning],
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for
Fine-Grained Visual Classification,
ECCV18(III: 365-380).
Springer DOI
1810
BibRef
Sun, M.[Ming],
Yuan, Y.C.[Yu-Chen],
Zhou, F.[Feng],
Ding, E.[Errui],
Multi-Attention Multi-Class Constraint for Fine-grained Image
Recognition,
ECCV18(XVI: 834-850).
Springer DOI
1810
BibRef
Simonelli, A.,
de Natale, F.G.B.,
Messelodi, S.,
Bulo, S.R.,
Increasingly Specialized Ensemble of Convolutional Neural Networks
for Fine-Grained Recognition,
ICIP18(594-598)
IEEE DOI
1809
Feature extraction, Training, Zinc, Automobiles, Birds,
Heating systems, Convolutional neural networks, attention analysis
BibRef
He, Y.H.[Yu-Hang],
Chen, L.[Long],
Chen, J.[Jianda],
Multi-task Relative Attribute Prediction by Incorporating Local Context
and Global Style Information,
BMVC16(xx-yy).
HTML Version.
1805
Relative attribute represents the correlation degree of one attribute
between an image pair.
BibRef
Sudowe, P.[Patrick],
Leibe, B.[Bastian],
PatchIt: Self-Supervised Network Weight Initialization for Fine-grained
Recognition,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Su, J.C.[Jong-Chyi],
Wu, C.Y.[Chen-Yun],
Jiang, H.Z.[Huai-Zu],
Maji, S.[Subhransu],
Reasoning About Fine-Grained Attribute Phrases Using Reference Games,
ICCV17(418-427)
IEEE DOI
1802
Phrases in human discourse about the secne.
image representation, image retrieval,
inference mechanisms, learning (artificial intelligence),
Visualization
BibRef
Cai, S.J.[Si-Jia],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
Higher-Order Integration of Hierarchical Convolutional Activations
for Fine-Grained Visual Categorization,
ICCV17(511-520)
IEEE DOI
1802
neural nets, polynomials, statistics, FGVC,
fine-grained visual categorization,
BibRef
Hou, S.,
Feng, Y.,
Wang, Z.,
VegFru: A Domain-Specific Dataset for Fine-Grained Visual
Categorization,
ICCV17(541-549)
IEEE DOI
1802
feature extraction, image fusion, image representation,
object recognition, CUB-200-2011, HybridNet, VegFru, animal breeds,
Visualization
BibRef
Gebru, T.,
Hoffman, J.,
Fei-Fei, L.,
Fine-Grained Recognition in the Wild:
A Multi-task Domain Adaptation Approach,
ICCV17(1358-1367)
IEEE DOI
1802
image annotation, image classification,
learning (artificial intelligence), object detection,
Training
BibRef
Dai, X.,
Southall, B.,
Trinh, N.,
Matei, B.,
Efficient Fine-Grained Classification and Part Localization Using One
Compact Network,
CEFR-LCV17(996-1004)
IEEE DOI
1802
Automobiles, Birds, Computer architecture, Convolution, Switches, Training
BibRef
Moshkelgosha, V.,
Behzadi-Khormouji, H.[Hamed],
Yazdian-Dehkordi, M.[Mahdi],
Coarse-to-fine parameter tuning for content-based object
categorization,
IPRIA17(160-165)
IEEE DOI
1712
feature extraction, image classification,
visual databases, COREL dataset,
k-nearest neighbor
BibRef
Kong, S.[Shu],
Fowlkes, C.C.[Charless C.],
Pixel-Wise Attentional Gating for Scene Parsing,
WACV19(1024-1033)
IEEE DOI
1904
BibRef
And:
Recurrent Scene Parsing with Perspective Understanding in the Loop,
CVPR18(956-965)
IEEE DOI
1812
Depth aware to deal with object scale.
convolutional neural nets, image segmentation,
learning (artificial intelligence), surface normal estimation,
Routing.
Semantics, Computer architecture, Training,
Task analysis, Computational modeling, Convolution
BibRef
Kong, S.[Shu],
Fowlkes, C.C.[Charless C.],
Recurrent Pixel Embedding for Instance Grouping,
CVPR18(9018-9028)
IEEE DOI
1812
Proposals, Training, Semantics, Image segmentation, Task analysis,
Measurement, Labeling
BibRef
Kong, S.[Shu],
Fowlkes, C.C.[Charless C.],
Low-Rank Bilinear Pooling for Fine-Grained Classification,
CVPR17(7025-7034)
IEEE DOI
1711
Computational modeling, Covariance matrices,
Eigenvalues and eigenfunctions, Feature extraction, Standards,
Support vector machines, Training
BibRef
Lopez-Paz, D.[David],
Nishihara, R.[Robert],
Chintala, S.[Soumith],
Schölkopf, B.[Bernhard],
Bottou, L.[Léon],
Discovering Causal Signals in Images,
CVPR17(58-66)
IEEE DOI
1711
Object categories in images.
Artificial intelligence, Automobiles, Bridges,
Facebook, Random variables, Wheels
BibRef
Zheng, H.,
Fu, J.,
Mei, T.,
Luo, J.,
Learning Multi-attention Convolutional Neural Network for
Fine-Grained Image Recognition,
ICCV17(5219-5227)
IEEE DOI
1802
feature extraction, image recognition, image representation,
learning (artificial intelligence), neural nets,
Visualization
BibRef
Fu, J.,
Zheng, H.,
Mei, T.,
Look Closer to See Better: Recurrent Attention Convolutional Neural
Network for Fine-Grained Image Recognition,
CVPR17(4476-4484)
IEEE DOI
1711
Birds, Feature extraction, Image recognition, Neural networks,
Proposals, Visualization
BibRef
Lam, M.,
Mahasseni, B.,
Todorovic, S.,
Fine-Grained Recognition as HSnet Search for Informative Image Parts,
CVPR17(6497-6506)
IEEE DOI
1711
Computer architecture, Feature extraction, Image recognition,
Object recognition, Proposals, Search problems, Trajectory
BibRef
Karlinsky, L.,
Shtok, J.,
Tzur, Y.,
Tzadok, A.,
Fine-Grained Recognition of Thousands of Object Categories with
Single-Example Training,
CVPR17(965-974)
IEEE DOI
1711
Detectors, Image recognition, Probabilistic logic,
Training, Training, data
BibRef
Zhang, J.Y.[Jia-Yi],
Xu, S.P.[Shi-Pu],
Liu, Y.[Yang],
Hao, Y.P.[Yong-Ping],
Research on the identification method of micro assembly part,
ICIVC17(295-298)
IEEE DOI
1708
Charge coupled devices, Control systems, Image edge detection,
Noise reduction, Shape, Smoothing methods, Visualization,
image recognition, micro assembly, template matching
BibRef
Takahashi, T.[Toru],
Kudo, Y.[Yuta],
Ishiyama, R.[Rui],
Mass-produced parts traceability system based on automated scanning
of 'Fingerprint of Things',
MVA17(202-206)
DOI Link
1708
Fasteners, Fingerprint recognition, Image matching, Imaging, Metals,
Mobile handsets, Prototypes. Too small for id tags.
BibRef
Oliveau, Q.[Quentin],
Sahbi, H.[Hichem],
Semantic-free attributes for image classification,
ICPR16(1577-1582)
IEEE DOI
1705
Mid-level features.
Mathematical model, Optimization, Semantics,
Support vector machines, Training, Training data, Visualization
BibRef
Dasgupta, R.,
Namboodiri, A.M.,
Leveraging multiple tasks to regularize fine-grained classification,
ICPR16(3476-3481)
IEEE DOI
1705
Feature extraction, Neural networks, Ontologies, Pipelines,
Pose estimation, Semantics, Training
BibRef
Chakraborti, T.,
McCane, B.,
Mills, S.,
Pal, U.,
Collaborative representation based fine-grained species recognition,
ICVNZ16(1-6)
IEEE DOI
1701
Birds
BibRef
Ge, Z.Y.[Zong-Yuan],
McCool, C.[Chris],
Sanderson, C.[Conrad],
Wang, P.[Peng],
Liu, L.Q.[Ling-Qiao],
Reid, I.D.[Ian D.],
Corke, P.[Peter],
Exploiting Temporal Information for DCNN-Based Fine-Grained Object
Classification,
DICTA16(1-6)
IEEE DOI
1701
Australia
BibRef
Ai, S.S.[Shan-Shan],
Jia, C.Y.[Cai-Yan],
Chen, Z.N.[Zhi-Neng],
Large-Scale Product Classification via Spatial Attention Based CNN
Learning and Multi-class Regression,
MMMod17(I: 176-188).
Springer DOI
1701
BibRef
Diba, A.[Ali],
Pazandeh, A.M.[Ali Mohammad],
Pirsiavash, H.[Hamed],
Van Gool, L.J.[Luc J.],
DeepCAMP: Deep Convolutional Action Attribute Mid-Level Patterns,
CVPR16(3557-3565)
IEEE DOI
1612
BibRef
Wang, Y.M.[Ya-Ming],
Choi, J.H.[Jong-Hyun],
Morariu, V.I.[Vlad I.],
Davis, L.S.[Larry S.],
Mining Discriminative Triplets of Patches for Fine-Grained
Classification,
CVPR16(1163-1172)
IEEE DOI
1612
BibRef
Nagaraja, V.K.[Varun K.],
Morariu, V.I.[Vlad I.],
Davis, L.S.[Larry S.],
Modeling Context Between Objects for Referring Expression Understanding,
ECCV16(IV: 792-807).
Springer DOI
1611
BibRef
Earlier:
Searching for Objects using Structure in Indoor Scenes,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Earlier:
Feedback Loop Between High Level Semantics and Low Level Vision,
GMCV14(485-499).
Springer DOI
1504
BibRef
Cui, Y.[Yin],
Zhou, F.[Feng],
Lin, Y.Q.[Yuan-Qing],
Belongie, S.J.[Serge J.],
Fine-Grained Categorization and Dataset Bootstrapping Using Deep
Metric Learning with Humans in the Loop,
CVPR16(1153-1162)
IEEE DOI
1612
BibRef
Qian, Q.[Qi],
Jin, R.[Rong],
Zhu, S.H.[Sheng-Huo],
Lin, Y.Q.[Yuan-Qing],
Fine-grained visual categorization via multi-stage metric learning,
CVPR15(3716-3724)
IEEE DOI
1510
BibRef
Zhang, H.[Han],
Xu, T.[Tao],
Elhoseiny, M.[Mohamed],
Huang, X.L.[Xiao-Lei],
Zhang, S.T.[Shao-Ting],
Elgammal, A.E.[Ahmed E.],
Metaxas, D.N.[Dimitris N.],
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for
Fine-Grained Recognition,
CVPR16(1143-1152)
IEEE DOI
1612
BibRef
Zhang, X.,
Zhou, F.,
Lin, Y.,
Zhang, S.,
Embedding Label Structures for Fine-Grained Feature Representation,
CVPR16(1114-1123)
IEEE DOI
1612
BibRef
Zhou, F.,
Lin, Y.,
Fine-Grained Image Classification by Exploring Bipartite-Graph Labels,
CVPR16(1124-1133)
IEEE DOI
1612
BibRef
Krause, J.[Jonathan],
Sapp, B.[Benjamin],
Howard, A.[Andrew],
Zhou, H.[Howard],
Toshev, A.[Alexander],
Duerig, T.[Tom],
Philbin, J.[James],
Fei-Fei, L.[Li],
The Unreasonable Effectiveness of Noisy Data for Fine-Grained
Recognition,
ECCV16(III: 301-320).
Springer DOI
1611
BibRef
George, M.[Marian],
Dixit, M.[Mandar],
Zogg, G.[Gábor],
Vasconcelos, N.M.[Nuno M.],
Semantic Clustering for Robust Fine-Grained Scene Recognition,
ECCV16(I: 783-798).
Springer DOI
1611
BibRef
Wang, X.,
Li, R.,
Currey, J.,
Leveraging 2D and 3D cues for fine-grained object classification,
ICIP16(1354-1358)
IEEE DOI
1610
Cameras
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Wang, Y.,
Zhang, X.Y.,
Zhang, Y.,
Hou, X.,
Liu, C.L.,
Exploiting coarse-to-fine mechanism for fine-grained recognition,
ICIP16(649-653)
IEEE DOI
1610
BibRef
Baz, I.,
Yoruk, E.,
Cetin, M.,
Context-aware hybrid classification system for fine-grained retail
product recognition,
IVMSP16(1-5)
IEEE DOI
1608
Computational modeling
BibRef
Wang, D.,
Shen, Z.,
Shao, J.,
Zhang, W.,
Xue, X.,
Zhang, Z.,
Multiple Granularity Descriptors for Fine-Grained Categorization,
ICCV15(2399-2406)
IEEE DOI
1602
Birds
BibRef
George, M.,
Mircic, D.,
Soros, G.,
Floerkemeier, C.,
Mattern, F.,
Fine-Grained Product Class Recognition for Assisted Shopping,
ACVR15(546-554)
IEEE DOI
1602
Histograms
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Aich, S.[Shubhra],
Lee, C.W.[Chil-Woo],
A General Vocabulary Based Approach for Fine-Grained Object Recognition,
PSIVT15(572-581).
Springer DOI
1602
BibRef
Mallya, A.[Arun],
Lazebnik, S.[Svetlana],
Learning Informative Edge Maps for Indoor Scene Layout Prediction,
ICCV15(936-944)
IEEE DOI
1602
Clutter
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Shih, K.J.[Kevin J.],
Mallya, A.[Arun],
Singh, S.[Saurabh],
Hoiem, D.[Derek],
Part Localization using Multi-Proposal Consensus for Fine-Grained
Categorization,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Chevalier, M.,
Thome, N.,
Cord, M.,
Fournier, J.,
Henaff, G.,
Dusch, E.,
LR-CNN for fine-grained classification with varying resolution,
ICIP15(3101-3105)
IEEE DOI
1512
Convolutional neural networks
BibRef
Ge, Z.[Zong_Yuan],
Bewley, A.,
McCool, C.[Chris],
Corke, P.[Peter],
Upcroft, B.,
Sanderson, C.[Conrad],
Fine-grained classification via mixture of deep convolutional neural
networks,
WACV16(1-6)
IEEE DOI
1606
BibRef
Earlier: A1, A3, A6, A4, Only:
Modelling local deep convolutional neural network features to improve
fine-grained image classification,
ICIP15(4112-4116)
IEEE DOI
1512
Gaussian mixture models
BibRef
Kiapour, M.H.[M. Hadi],
Di, W.[Wei],
Jagadeesh, V.[Vignesh],
Piramuthu, R.[Robinson],
Mine the fine: Fine-grained fragment discovery,
ICIP15(3555-3559)
IEEE DOI
1512
Fine-grained; classification; deep learning; mid-level representation
BibRef
Sun, G.[Gang],
Chen, Y.Y.[Yan-Yun],
Liu, X.H.[Xue-Hui],
Wu, E.[Enhua],
Adaptive multi-task learning for fine-grained categorization,
ICIP15(996-1000)
IEEE DOI
1512
Multi-task learning; fine-grained; image categorization; low-rank
BibRef
Li, D.[Dong],
Li, Y.[Yali],
Wang, S.J.[Sheng-Jin],
Selective parts for fine-grained recognition,
ICIP15(922-926)
IEEE DOI
1512
Fine-grained recognition
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Liao, L.[Liang],
Hu, R.M.[Rui-Min],
Xiao, J.[Jun],
Wang, Q.[Qi],
Xiao, J.[Jing],
Chen, J.[Jun],
Exploiting effects of parts in fine-grained categorization of
vehicles,
ICIP15(745-749)
IEEE DOI
1512
DPM; SVM; fine-grained categorization; vehicle parts
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Krapac, J.[Josip],
egvic, S.[Sinia],
Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification
with Large Fisher Vectors,
GCPR15(470-480).
Springer DOI
1511
BibRef
Lin, D.[Di],
Shen, X.Y.[Xiao-Yong],
Lu, C.[Cewu],
Jia, J.Y.[Jia-Ya],
Deep LAC: Deep localization, alignment and classification for
fine-grained recognition,
CVPR15(1666-1674)
IEEE DOI
1510
part localization, alignment, and classification in one deep neural network.
BibRef
Xie, S.N.[Sai-Ning],
Yang, T.B.[Tian-Bao],
Wang, X.Y.[Xiao-Yu],
Lin, Y.Q.[Yuan-Qing],
Hyper-class augmented and regularized deep learning for fine-grained
image classification,
CVPR15(2645-2654)
IEEE DOI
1510
BibRef
Ge, Z.[Zong_Yuan],
McCool, C.[Christopher],
Sanderson, C.[Conrad],
Corke, P.[Peter],
Subset feature learning for fine-grained category classification,
DeepLearn15(46-52)
IEEE DOI
1510
Accuracy
BibRef
Reed, S.[Scott],
Akata, Z.[Zeynep],
Lee, H.L.[Hong-Lak],
Schiele, B.[Bernt],
Learning Deep Representations of Fine-Grained Visual Descriptions,
CVPR16(49-58)
IEEE DOI
1612
BibRef
Akata, Z.[Zeynep],
Reed, S.[Scott],
Walter, D.[Daniel],
Lee, H.L.[Hong-Lak],
Schiele, B.[Bernt],
Evaluation of output embeddings for fine-grained image classification,
CVPR15(2927-2936)
IEEE DOI
1510
BibRef
Krause, J.[Jonathan],
Jin, H.L.[Hai-Lin],
Yang, J.C.[Jian-Chao],
Fei-Fei, L.[Li],
Fine-grained recognition without part annotations,
CVPR15(5546-5555)
IEEE DOI
1510
BibRef
Chen, G.[Guang],
Yang, J.C.[Jian-Chao],
Jin, H.L.[Hai-Lin],
Shechtman, E.[Eli],
Brandt, J.[Jonathan],
Han, T.X.[Ton X.],
Selective Pooling Vector for Fine-Grained Recognition,
WACV15(860-867)
IEEE DOI
1503
Approximation methods. Selectively pooling local descriptors.
E.g. which bird, which dog.
BibRef
Pu, J.[Jian],
Jiang, Y.G.[Yu-Gang],
Wang, J.[Jun],
Xue, X.Y.[Xiang-Yang],
Which Looks Like Which: Exploring Inter-class Relationships in
Fine-Grained Visual Categorization,
ECCV14(III: 425-440).
Springer DOI
1408
BibRef
Zheng, Y.B.[Ying-Bin],
Jiang, Y.G.[Yu-Gang],
Xue, X.Y.[Xiang-Yang],
Learning Hybrid Part Filters for Scene Recognition,
ECCV12(V: 172-185).
Springer DOI
1210
Not the whole object, but parts that may be shared with multiple objects.
BibRef
Mottos, A.B.[Andrea Britto],
Feris, R.S.[Rogerio Schmidt],
Fusing well-crafted feature descriptors for efficient fine-grained
classification,
ICIP14(5197-5201)
IEEE DOI
1502
E.g. plants or insects on smart phone.
BibRef
Vedaldi, A.[Andrea],
Mahendran, S.[Siddharth],
Tsogkas, S.[Stavros],
Maji, S.[Subhransu],
Girshick, R.[Ross],
Kannala, J.H.[Ju-Ho],
Rahtu, E.[Esa],
Kokkinos, I.[Iasonas],
Blaschko, M.B.[Matthew B.],
Weiss, D.[David],
Taskar, B.[Ben],
Simonyan, K.[Karen],
Saphra, N.[Naomi],
Mohamed, S.[Sammy],
Understanding Objects in Detail with Fine-Grained Attributes,
CVPR14(3622-3629)
IEEE DOI
1409
attribute
BibRef
Zhang, N.[Ning],
Donahue, J.[Jeff],
Girshick, R.[Ross],
Darrell, T.J.[Trevor J.],
Part-Based R-CNNs for Fine-Grained Category Detection,
ECCV14(I: 834-849).
Springer DOI
1408
BibRef
Kanan, C.[Christopher],
Fine-grained object recognition with Gnostic Fields,
WACV14(23-30)
IEEE DOI
1406
Accuracy. What kind of duck, not duck vs. desk.
BibRef
Ordonez, V.[Vicente],
Jagadeesh, V.[Vignesh],
Di, W.[Wei],
Bhardwaj, A.[Anurag],
Piramuthu, R.[Robinson],
Furniture-geek: Understanding fine-grained furniture attributes from
freely associated text and tags,
WACV14(317-324)
IEEE DOI
1406
Calibration; HTML; Image color analysis; Predictive models
See also Style Finder: Fine-Grained Clothing Style Detection and Retrieval.
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
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