Fetching the paper…
Reading the bibliography…
Fine-grained object classification is a challenging task due to the subtle inter-class difference and large intra-class variation.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Mach. Learn. , vol. 8, no. 3-4, pp. 229–256, May 1992
1992
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. , vol. 9, no. 8, pp. 1735–1780, Nov. 1997
1997
Earlier work this paper cites.
R. A. Rensink, “The dynamic representation of scenes,” Visual Cognition , vol. 7, no. 1-3, pp. 17–42, 2000
2000
Earlier work this paper cites.
L. Bo, X. Ren, and D. Fox, “Kernel descriptors for visual recognition,” in Proc. Adv. Neural Inf. Process. Syst. , Dec. 2010, pp. 244–252
2010
Earlier work this paper cites.
S. Branson, C. Wah, F. Schroff, B. Babenko, P. Welinder, P. Perona, and S. Belongie, “Visual recognition with humans in the loop,” in Proc. IEEE Euro. Conf. Comput. Vis. , 2010, pp. 438–451
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 L. Fei-Fei, “Novel dataset for fine-grained image categorization,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recog. Workshops , Jun. 2011, pp. 3466–3473
2011
Earlier work this paper cites.
Y. Chai, V. Lempitsky, and A. Zisserman, “Bicos: A bi-level co-segmentation method for image classification,” in Proc. IEEE Int. Conf. Comput. Vis. , Nov. 2011, pp. 2579–2586
2011
Earlier work this paper cites.
J. Liu, A. Kanazawa, D. Jacobs, and P. Belhumeur, “Dog breed classification using part localization,” in Proc. IEEE Euro. Conf. Comput. Vis. , Oct. 2012, pp. 172–185
2012
Earlier work this paper cites.
B. Yao, “A codebook-free and annotation-free approach for fine-grained image categorization,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2012, pp. 3466–3473
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst. , 2012, pp. 1097–1105
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 Adv. Neural Inf. Process. Syst. , 2012, pp. 3122–3130
2012
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in Proc. IEEE Int. Conf. Comput. Vis. Workshops , Jun. 2013, pp. 554–561
2013
Earlier work this paper cites.
X. Wang, T. Zhang, D. R. Tretter, and Q. Lin, “Personal clothing retrieval on photo collections by color and attributes,” IEEE Trans Multimedia , vol. 15, no. 8, pp. 2035–2045, Dec. 2013
2013
Earlier work this paper cites.
T. Berg and P. N. Belhumeur, “POOF: Part-Based One-vs-One Features for fine-grained categorization, face verification, and attribute estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2013, pp. 955–962
2013
Earlier work this paper cites.
Y. Chai, V. Lempitsky, and A. Zisserman, “Symbiotic segmentation and part localization for fine-grained categorization,” in Proc. IEEE Int. Conf. Comput. Vis. , Dec. 2013, pp. 321–328
2013
Earlier work this paper cites.
T. V. Nguyen, B. Ni, H. Liu, W. Xia, J. Luo, M. Kankanhalli, and S. Yan, “Image re-attentionizing,” IEEE Trans. Multimedia , vol. 15, no. 8, pp. 1910–1919, Dec. 2013
2013
Earlier work this paper cites.
E. Gavves, B. Fernando, C. G. M. Snoek, A. W. M. Smeulders, and T. Tuytelaars, “Fine-grained categorization by alignments,” in Proc. IEEE Int. Conf. Comput. Vis. , Dec. 2013, pp. 1713–1720
2013
Earlier work this paper cites.
J. Deng, J. Krause, and L. Fei-Fei, “Fine-grained crowdsourcing for fine-grained recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , Jun. 2013, pp. 580–587
2013
Earlier work this paper cites.
J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” Int. J. Comput. Vis. , vol. 104, no. 2, pp. 154–171, 2013
2013
Cited alongside, same era.
S. Branson, G. V. Horn, S. Belongie, and P. Perona, “Bird species categorization using pose normalized deep convolutional nets,” in Proc. British Mach. Vis. Conf. , 2014, pp. 1–14
2014
Cited alongside, same era.
N. Zhang, J. Donahue, R. B. Girshick, and T. Darrell, “Part-based r-cnns for fine-grained category detection,” in Proc. IEEE Euro. Conf. Comput. Vis. , 2014, pp. 834–849
2014
Cited alongside, same era.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” in Proc. Int. Conf. Mach. Learning , 2014, pp. 647–655
2014
Cited alongside, same era.
S. Sharma, R. Kiros, and R. Salakhutdinov, “Action recognition using visual attention,” in NIPS Time Series Workshop , Dec. 2015
2015
Later among the works it cites.
J. Ba, V. Mnih, and K. Kavukcuoglu, “Multiple object recognition with visual attention,” in Proc. Int. Conf. Learning Representations , May 2015, pp. 1–10
2015
Later among the works it cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. Int. Conf. Learning Representations , May 2015
2015
Later among the works it cites.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in Proc. IEEE Int. Conf. Comput. Vis. , 2015, pp. 1449–1457
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Branson, G. Van Horn, P. Perona, and S. J. Belongie, “Improved bird species recognition using pose normalized deep convolutional nets.” in Proc. British Mach. Vis. Conf. , Sep. 2014, pp. 1–14
2014
Cited alongside, same era.
C. Wah, G. V. Horn, S. Branson, S. Maji, P. Perona, and S. Belongie, “Similarity comparisons for interactive fine-grained categorization,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , Jun. 2014, pp. 859–866
2014
Cited alongside, same era.
V. Mnih, N. Heess, A. Graves, and k. kavukcuoglu, “Recurrent models of visual attention,” in Proc. Adv. Neural Inf. Process. Syst. , 2014, pp. 2204–2212
2014
Cited alongside, same era.
2014
Cited alongside, same era.
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: Delving deep into convolutional nets,” in British Mach. Vis. Conf. , 2014
2014
Cited alongside, same era.
J. Krause, T. Gebru, J. Deng, L. J. Li, and L. Fei-Fei, “Learning features and parts for fine-grained recognition,” in Proc. Int. Conf. Pattern Recog. , Aug. 2014, pp. 26–33
2014
Cited alongside, same era.
P.-H. Gosselin, N. Murray, H. Jégou, and F. Perronnin, “Revisiting the fisher vector for fine-grained classification,” Pattern Recog. Letters , vol. 49, pp. 92–98, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Later among the works it cites.
Z. Ge, C. McCool, C. Sanderson, and P. I. Corke, “Subset feature learning for fine-grained category classification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2015, pp. 46–52
2015
Later among the works it cites.
M. Simon and E. Rodner, “Neural activation constellations: Unsupervised part model discovery with convolutional networks,” in Proc. IEEE Int. Conf. Comput. Vis. , 2015
2015
Later among the works it cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Mach. Learning , 2015, pp. 448–456
2015
Later among the works it cites.
E. Gavves, B. Fernando, C. G. M. Snoek, A. W. M. Smeulders, and T. Tuytelaars, “Local alignments for fine-grained categorization,” Int. J. of Comput. Vis. , vol. 111, no. 2, pp. 191–212, 2015
2015
Later among the works it cites.
X. Liang, L. Lin, W. Yang, P. Luo, J. Huang, and S. Yan, “Clothes co-parsing via joint image segmentation and labeling with application to clothing retrieval,” IEEE Trans. Multimedia , vol. 18, no. 6, pp. 1175–1186, Jun. 2016
2016
Closest in time.
M. Bolaños and P. Radeva, “Simultaneous food localization and recognition,” in Proc. Int. Conf. Pattern Recog. , 2016
2016
Closest in time.
Y. Wei, W. Xia, M. Lin, J. Huang, B. Ni, J. Dong, Y. Zhao, and S. Yan, “Hcp: A flexible cnn framework for multi-label image classification,” IEEE Trans. Pattern Recog. and Mach. Intell. , vol. 38, no. 9, pp. 1901–1907, 2016
2016
Closest in time.
2016
Closest in time.
C. Luo, B. Ni, S. Yan, and M. Wang, “Image classification by selective regularized subspace learning,” IEEE Trans. Multimedia , vol. 18, no. 1, pp. 40–50, Jan. 2016
2016
Closest in time.
X. Zhang, H. Xiong, W. Zhou, W. Lin, and Q. Tian, “Picking deep filter responses for fine-grained image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , June 2016, pp. 1134–1142
2016
Closest in time.
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and F. Li, “The unreasonable effectiveness of noisy data for fine-grained recognition,” in Proc. IEEE Euro. Conf. Comput. Vis. , Oct. 2016, pp. 301–320
2016
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman, and k. kavukcuoglu, “Spatial transformer networks,” in Adv. Neural Inf. Process. Syst. , 2015, pp. 2017–2025
2025
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in Int. Conf. Mach. Learning , 2015, pp. 2048–2057
2057
Closest in time.