13.6.8.1.1 Fine-Grained Classification Using CNN, Convolutional Neural Networks

Chapter Contents (Back)
Context. CNN. Convolutional Neural Networks. Fine-Grained.

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

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

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

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

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.T.[Han-Tao], Zhang, S.L.[Shi-Liang], Yan, C.G.[Cheng-Gang], Zhang, Y.D.[Yong-Dong], Li, J.T.[Jin-Tao], Tian, Q.[Qi],
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

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 retrieval, neural nets, SCDA feature visualization, SCDA method, deep convolutional neural network model, fine-grained image retrieval, imageNet classification, selective convolutional descriptor aggregation, state-of-the-art general image retrieval approach, unsupervised retrieval task, visual attribute, Automobiles, Birds, Buildings, Convolution, Image retrieval, Machine learning, Fine-grained image retrieval, selection and aggregation, unsupervised object localization 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

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

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

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

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

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

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

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

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

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

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

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

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, 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

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

Zhu, J.W.[Jian-Wei], Li, Z.X.[Zhi-Xin], Wei, J.[Jiahui], Zeng, Y.F.[Yu-Fei], Ma, H.F.[Hui-Fang],
Fine-grained bidirectional attentional generation and knowledge-assisted networks for cross-modal retrieval,
IVC(124), 2022, pp. 104507.
Elsevier DOI 2208
Cross-modal retrieval, Graph convolutional network, Knowledge embedding, Cross-attention, Attentional generative network BibRef

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

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

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

Xu, Q.[Qin], Zhang, M.Q.[Meng-Quan], Li, Y.[Yun], Tao, Z.[Zhifu],
Learning more discriminative clues with gradual attention for fine-grained visual categorization,
IVC(136), 2023, pp. 104753.
Elsevier DOI 2308
Fine-grained visual categorization, Convolutional neural network, Visual attention, Self-calibrated convolution BibRef

Yu, H.[Han], Lu, H.[Huibin], Zhao, M.[Min], Li, Z.Y.[Zhuo-Yi], Gu, G.H.[Guang-Hua],
Gradient aggregation based fine-grained image retrieval: A unified viewpoint for CNN and Transformer,
PR(149), 2024, pp. 110248.
Elsevier DOI 2403
A discriminative representation hides in the gradients of convolution filters. Fine-grained image retrieval, Convolution filters gradient aggregation, CFGA feature, Deep metric learning 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

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

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

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

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

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

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

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

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

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

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

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

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

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
BibRef

Ai, S.S.[Shan-Shan], Jia, C.Y.[Cai-Yan], Chen, Z.N.[Zhi-Neng],
Large-Scale Product Classification via Spatial Attention Based CNN Learning and Multi-class Regression,
MMMod17(I: 176-188).
Springer DOI 1701
BibRef

Diba, A.[Ali], Pazandeh, A.M.[Ali Mohammad], Pirsiavash, H.[Hamed], Van Gool, L.J.[Luc J.],
DeepCAMP: Deep Convolutional Action Attribute Mid-Level Patterns,
CVPR16(3557-3565)
IEEE DOI 1612
BibRef

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

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

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

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
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Last update:Apr 10, 2024 at 09:54:40