13.6.8.1 Context, Fine-Grained Classification

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., Gonzàlez, 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
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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
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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.[Yufei], 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

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

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

Šipka, T.[Tomáš], Šulc, 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.[Yajie], Zhang, M.[Miaohua], 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], Gonzàlez, 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
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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
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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
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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
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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
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Wang, X., Li, R., Currey, J.,
Leveraging 2D and 3D cues for fine-grained object classification,
ICIP16(1354-1358)
IEEE DOI 1610
Cameras BibRef

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
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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 BibRef

Aich, S.[Shubhra], Lee, C.W.[Chil-Woo],
A General Vocabulary Based Approach for Fine-Grained Object Recognition,
PSIVT15(572-581).
Springer DOI 1602
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Mallya, A.[Arun], Lazebnik, S.[Svetlana],
Learning Informative Edge Maps for Indoor Scene Layout Prediction,
ICCV15(936-944)
IEEE DOI 1602
Clutter BibRef

Shih, K.J.[Kevin J.], Mallya, A.[Arun], Singh, S.[Saurabh], Hoiem, D.[Derek],
Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization,
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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 BibRef

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 BibRef

Krapac, J.[Josip], Šegvic, S.[Siniša],
Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification with Large Fisher Vectors,
GCPR15(470-480).
Springer DOI 1511
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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
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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
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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
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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
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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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