Fetching the paper…
Reading the bibliography…
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses.
E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes-Braem, “Basic objects in natural categories,”
1976
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,”
1992
Earlier work this paper cites.
R. S. Sutton, D. A. McAllester, S. P. Singh, Y. Mansour
1999
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in
2008
Earlier work this paper cites.
L. Bourdev and J. Malik, “Poselets: Body part detectors trained using 3d human pose annotations,” in
2009
Earlier work this paper cites.
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,”
2010
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li, “Novel dataset for fine-grained image categorization: Stanford dogs,” in
2011
Earlier work this paper cites.
R. Farrell, O. Oza, N. Zhang, V. I. Morariu, T. Darrell, and L. S. Davis, “Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance,” in
2011
Earlier work this paper cites.
Y. Chai, V. Lempitsky, and A. Zisserman, “Bicos: A bi-level co-segmentation method for image classification,” in
2011
Earlier work this paper cites.
N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares, “Leafsnap: A computer vision system for automatic plant species identification,” in
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Earlier work this paper cites.
J. Liu, A. Kanazawa, D. Jacobs, and P. Belhumeur, “Dog breed classification using part localization,” in
2012
Earlier work this paper cites.
S. Yang, L. Bo, J. Wang, and L. G. Shapiro, “Unsupervised template learning for fine-grained object recognition,” in
2012
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in
2013
Earlier work this paper cites.
N. Zhang, R. Farrell, F. Iandola, and T. Darrell, “Deformable part descriptors for fine-grained recognition and attribute prediction,” in
2013
Earlier work this paper cites.
T. Berg and P. Belhumeur, “Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation,” in
2013
Earlier work this paper cites.
E. Gavves, B. Fernando, C. G. Snoek, A. W. Smeulders, and T. Tuytelaars, “Fine-grained categorization by alignments,” in
2013
Cited alongside, same era.
Y. Chai, V. Lempitsky, and A. Zisserman, “Symbiotic segmentation and part localization for fine-grained categorization,” in
2013
Cited alongside, same era.
L. Bossard, M. Guillaumin, and L. Van Gool, “Food-101–mining discriminative components with random forests,” in
2014
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Cited alongside, same era.
2014
Cited alongside, same era.
R. Girshick, “Fast r-cnn,” in
2015
Later among the works it cites.
J. Krause, H. Jin, J. Yang, and L. Fei-Fei, “Fine-grained recognition without part annotations,” in
2015
Later among the works it cites.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in
2015
Later among the works it cites.
Q. Qian, R. Jin, S. Zhu, and Y. Lin, “Fine-grained visual categorization via multi-stage metric learning,” in
2015
Later among the works it cites.
M. Simon and E. Rodner, “Neural activation constellations: Unsupervised part model discovery with convolutional networks,” in
2015
Later among the works it cites.
Y. Zhang, X.-s. Wei, J. Wu, J. Cai, J. Lu, V. Nguyen, and M. Do, “Weakly supervised fine-grained image categorization. arxiv preprint,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Mnih, N. Heess, A. Graves
2014
Cited alongside, same era.
P. Sermanet, A. Frome, and E. Real, “Attention for fine-grained categorization,”
2014
Cited alongside, same era.
2014
Cited alongside, same era.
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev, “Panda: Pose aligned networks for deep attribute modeling,” in
2014
Cited alongside, same era.
N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-based r-cnns for fine-grained category detection,” in
2014
Cited alongside, same era.
J. Ba, V. Mnih, and K. Kavukcuoglu, “Multiple object recognition with visual attention,”
2014
Cited alongside, same era.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in
2014
Cited alongside, same era.
2015
Later among the works it cites.
A. Meyers, N. Johnston, V. Rathod, A. Korattikara, A. Gorban, N. Silberman, S. Guadarrama, G. Papandreou, J. Huang, and K. P. Murphy, “Im2calories: towards an automated mobile vision food diary,” in
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Closest in time.
Y. Cui, F. Zhou, Y. Lin, and S. Belongie, “Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop,” in
2016
Closest in time.
S. Huang, Z. Xu, D. Tao, and Y. Zhang, “Part-stacked cnn for fine-grained visual categorization,” in
2016
Closest in time.
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell, “Compact bilinear pooling,” in
2016
Closest in time.
S. Kong and C. Fowlkes, “Low-rank bilinear pooling for fine-grained classification,”
2016
Closest in time.
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei, “The unreasonable effectiveness of noisy data for fine-grained recognition,” in
2016
Closest in time.
Y. Wang, J. Choi, V. Morariu, and L. S. Davis, “Mining discriminative triplets of patches for fine-grained classification,” in
2016
Closest in time.
T. Berg, J. Liu, S. Woo Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur, “Birdsnap: Large-scale fine-grained visual categorization of birds,” in
2018
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman
2025
Closest in time.