13.6.8.1 Context, Fine-Grained Classification

Chapter Contents (Back)
Matching, Context. Context. Fine-Grained.

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 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., Wei, X.S., Wu, J., Cai, J., Lu, J., Nguyen, V.A., Do, M.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., Wu, X., Feng, J., Peng, Q., Yan, S.,
Diversified Visual Attention Networks for Fine-Grained Object Classification,
MultMed(19), No. 6, June 2017, pp. 1245-1256.
IEEE DOI 1705
Birds, Diversity reception, Dogs, Feature extraction, Predictive models, Training, Visualization, Deep learning, fine-grained object classification, long-short-term-memory (LSTM), visual, attention BibRef

Zhao, B., Feng, J., Wu, X., Yan, S.,
Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search,
CVPR17(6156-6164)
IEEE DOI 1711
Clothing, Computer architecture, Image color analysis, Image representation, Prototypes, Search engines, Visualization 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
computer vision, 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.[Liwei],
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., Wang, Y., Li, X., Gao, J.,
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

Qi, L.[Lei], Lu, X.[Xiaoqiang], 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., Peng, Y., Zhao, J.,
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, Computer vision, Machine learning, multi-level attention. Image recognition, Natural languages, Semantics, Streaming media, Visualization 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


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
computer vision, 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

Xu, Q.[Qin], Sun, Y.F.[Yi-Fan], Li, Y.L.[Ya-Li], Wang, S.J.[Sheng-Jin],
Attend and Align: Improving Deep Representations with Feature Alignment Layer for Person Retrieval,
ICPR18(2148-2153)
IEEE DOI 1812
Training, Noise measurement, Task analysis, Pipelines, Spatial resolution, Testing 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

Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.,
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.[Xinyi], Zheng, Q.[Qi], Zhang, P.[Peng], You, X.[Xinge],
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.[Kaiyu], 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., 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. computer vision, image representation, image retrieval, inference mechanisms, learning (artificial intelligence), Visualization BibRef

Cai, S., Zuo, W., Zhang, L.,
Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization,
ICCV17(511-520)
IEEE DOI 1802
computer vision, 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
computer vision, image annotation, image classification, learning (artificial intelligence), object detection, Training BibRef

Bao, J., Chen, D., Wen, F., Li, H., Hua, G.,
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training,
ICCV17(2764-2773)
IEEE DOI 1802
entropy, face recognition, feature extraction, image classification, image coding, image matching, 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., Yazdian-Dehkordi, M.[Mahdi],
Coarse-to-fine parameter tuning for content-based object categorization,
IPRIA17(160-165)
IEEE DOI 1712
computer vision, 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

Feng, W.[Wu], Liu, D.[Dong],
Fine-Grained Image Recognition from Click-Through Logs Using Deep Siamese Network,
MMMod17(I: 127-138).
Springer DOI 1701
Need large scale labeled datasets for training. Dog breeds. 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.[Nuno],
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 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
Conferences 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 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
BibRef

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,
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 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
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

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

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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Last update:Jun 19, 2019 at 09:55:06