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Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional operations.
G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in CVPR , 2017, pp. 1925–1934
1934
Earlier work this paper cites.
L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology , vol. 26, no. 3, pp. 297–302, 1945
1945
Earlier work this paper cites.
T. J. Sørensen, A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons . I kommission hos E. Munksgaard, 1948
1948
Earlier work this paper cites.
D. Shepard, “A two-dimensional interpolation function for irregularly-spaced data,” in ACM , 1968, pp. 517–524
1968
Earlier work this paper cites.
J. C. Gower, “A general coefficient of similarity and some of its properties,” Biometrics , pp. 857–871, 1971
1971
Earlier work this paper cites.
R. D. Luce, “The choice axiom after twenty years,” Elsevier J. Math Psychol. , vol. 15, no. 3, pp. 215–233, 1977
1977
Earlier work this paper cites.
H. Akima, “A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points,” ACM TOMS , vol. 4, no. 2, pp. 148–159, 1978
1978
Earlier work this paper cites.
R. Franke, “Scattered data interpolation: Tests of some methods,” J. Math. of comp. , vol. 38, no. 157, pp. 181–200, 1982
1982
Earlier work this paper cites.
B. V. Kumar and L. Hassebrook, “Performance measures for correlation filters,” Applied optics , vol. 29, no. 20, pp. 2997–3006, 1990
1990
Earlier work this paper cites.
P. J. Huber, “Robust estimation of a location parameter,” in Breakthroughs in statistics . Springer, 1992, pp. 492–518
1992
Earlier work this paper cites.
M. Riesenhuber and T. Poggio, “Hierarchical models of object recognition in cortex,” Nature neuroscience , vol. 2, no. 11, pp. 1019–1025, 1999
1999
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in ICCV , vol. 2, 2001, pp. 416–423
2001
Earlier work this paper cites.
G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in ECCVW , 2004, pp. 1–22
2004
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE TIP , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
T. Serre, L. Wolf, and T. Poggio, “Object recognition with features inspired by visual cortex,” in CVPR , 2005, pp. 994–1000
2005
Earlier work this paper cites.
F. Van Der Heijden, R. P. Duin, D. De Ridder, and D. M. Tax, Classification, parameter estimation and state estimation: an engineering approach using MATLAB . John Wiley & Sons, 2005
2005
Earlier work this paper cites.
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in CVPR , 2006, pp. 2169–2178
2006
Earlier work this paper cites.
S.-H. Cha, “Comprehensive survey on distance/similarity measures between probability density functions,” IJMMAS , vol. 1, no. 2, p. 1, 2007
2007
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in CVPR , 2009, pp. 1794–1801
2009
Earlier work this paper cites.
H. Takeda, P. Milanfar, M. Protter, and M. Elad, “Super-resolution without explicit subpixel motion estimation,” IEEE TIP , vol. 18, no. 9, pp. 1958–1975, 2009
2009
Earlier work this paper cites.
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in CVPR , 2010, pp. 3360–3367
2010
Earlier work this paper cites.
Y.-L. Boureau, J. Ponce, and Y. LeCun, “A theoretical analysis of feature pooling in visual recognition,” in ICML , 2010, pp. 111–118
2010
Earlier work this paper cites.
V. W. Lee, C. Kim, J. Chhugani, M. Deisher, D. Kim, A. D. Nguyen, N. Satish, M. Smelyanskiy, S. Chennupaty, P. Hammarlund et al. , “Debunking the 100x GPU vs. CPU myth: An evaluation of throughput computing on CPU and GPU,” in ISCA , 2010
2010
Earlier work this paper cites.
S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and R. Szeliski, “A database and evaluation methodology for optical flow,” IJCV , vol. 92, no. 1, pp. 1–31, 2011
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
P. Domingos, “A few useful things to know about machine learning,” Communications of the ACM , vol. 55, no. 10, pp. 78–87, 2012
2012
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Stochastic pooling for regularization of deep convolutional neural networks,” in ICLR , 2013
2013
Earlier work this paper cites.
C. Liu and D. Sun, “On bayesian adaptive video super resolution,” IEEE TPAMI , vol. 36, no. 2, pp. 346–360, 2013
2013
Earlier work this paper cites.
J. B. Estrach, A. Szlam, and Y. LeCun, “Signal recovery from pooling representations,” in ICML , 2014, pp. 307–315
2014
Earlier work this paper cites.
C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio, “Learned-norm pooling for deep feedforward and recurrent neural networks,” in ECML PKDD , 2014, pp. 530–546
2014
Earlier work this paper cites.
D. Yu, H. Wang, P. Chen, and Z. Wei, “Mixed pooling for convolutional neural networks,” in RSKT , 2014, pp. 364–375
2014
Cited alongside, same era.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in ECCV , 2014, pp. 740–755
2014
Cited alongside, same era.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in ECCV , 2014, pp. 184–199
2014
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in CVPR , 2015, pp. 1–9
2015
Cited alongside, same era.
J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in CVPR , 2015, pp. 5197–5206
2015
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Later among the works it cites.
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in ECCV , 2018
2018
Later among the works it cites.
Z. Gao, L. Wang, and G. Wu, “LIP: Local importance-based pooling,” in ICCV , 2019
2019
Later among the works it cites.
W. Bao, W.-S. Lai, C. Ma, X. Zhang, Z. Gao, and M.-H. Yang, “Depth-aware video frame interpolation,” in CVPR , 2019, pp. 3703–3712
2019
Later among the works it cites.
Y.-L. Liu, Y.-T. Liao, Y.-Y. Lin, and Y.-Y. Chuang, “Deep video frame interpolation using cyclic frame generation,” in AAAI , vol. 33, no. 01, 2019, pp. 8794–8802
2019
Later among the works it cites.
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Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” IJCV , vol. 115, no. 3, pp. 211–252, 2015
2015
Cited alongside, same era.
C.-Y. Lee, P. W. Gallagher, and Z. Tu, “Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree,” in AISTATS , 2016, pp. 464–472
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” in BMVC , 2016, pp. 87.1–87.12
2016
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in CVPR , 2016, pp. 2818–2826
2016
Cited alongside, same era.
S. Zhai, H. Wu, A. Kumar, Y. Cheng, Y. Lu, Z. Zhang, and R. Feris, “S3pool: Pooling with stochastic spatial sampling,” in CVPR , 2017, pp. 4970–4978
2017
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE TPAMI , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Cited alongside, same era.
P. Yi, Z. Wang, K. Jiang, J. Jiang, and J. Ma, “Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations,” in ICCV , 2019, pp. 3106–3115
2019
Later among the works it cites.
T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman, “Video enhancement with task-oriented flow,” IJCV , vol. 127, no. 8, pp. 1106–1125, 2019
2019
Later among the works it cites.
H. Zhao, A. Torralba, L. Torresani, and Z. Yan, “HACS: Human action clips and segments dataset for recognition and temporal localization,” in ICCV , 2019, pp. 8668–8678
2019
Later among the works it cites.
2019
Later among the works it cites.
D. Tran, H. Wang, L. Torresani, and M. Feiszli, “Video classification with channel-separated convolutional networks,” in ICCV , 2019, pp. 5552–5561
2019
Later among the works it cites.
C. Feichtenhofer, H. Fan, J. Malik, and K. He, “SlowFast networks for video recognition,” in ICCV , 2019, pp. 6202–6211
2019
Later among the works it cites.
——, “Analyzing human-human interactions: A survey,” CVIU , vol. 188, p. 102799, 2019
2019
Later among the works it cites.
T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang, “Second-order attention network for single image super-resolution,” in CVPR , 2019, pp. 11 065–11 074
2019
Later among the works it cites.
W. Li, X. Tao, T. Guo, L. Qi, J. Lu, and J. Jia, “Mucan: Multi-correspondence aggregation network for video super-resolution,” in ECCV . Springer, 2020, pp. 335–351
2020
Later among the works it cites.
Z. Wang, J. Chen, and S. C. Hoi, “Deep learning for image super-resolution: A survey,” IEEE TPAMI , vol. 43, no. 10, pp. 3365–3387, 2020
2020
Later among the works it cites.
——, “Softmax splatting for video frame interpolation,” in CVPR , 2020, pp. 5437–5446
2020
Later among the works it cites.
A. Mercat, M. Viitanen, and J. Vanne, “UVG dataset: 50/120fps 4K sequences for video codec analysis and development,” in ACMMSys , 2020, pp. 297–302
2020
Later among the works it cites.
C.-Y. Wu, R. Girshick, K. He, C. Feichtenhofer, and P. Krähenbühl, “A multigrid method for efficiently training video models,” in CVPR , 2020, pp. 153–162
2020
Later among the works it cites.
2020
Later among the works it cites.
B. Niu, W. Wen, W. Ren, X. Zhang, L. Yang, S. Wang, K. Zhang, X. Cao, and H. Shen, “Single image super-resolution via a holistic attention network,” in ECCV , 2020, pp. 191–207
2020
Later among the works it cites.
M. Choi, H. Kim, B. Han, N. Xu, and K. M. Lee, “Channel attention is all you need for video frame interpolation,” in AAAI , vol. 34, no. 07, 2020, pp. 10 663–10 671
2020
Later among the works it cites.
J. Park, K. Ko, C. Lee, and C.-S. Kim, “BMBC: Bilateral motion estimation with bilateral cost volume for video interpolation,” in ECCV , 2020, pp. 109–125
2020
Later among the works it cites.
A. Stergiou, R. Poppe, and K. Grigorios, “Refining activation downsampling with softpool,” in ICCV , 2021, pp. 10 357–10 366
2021
Closest in time.
J. Zhao and C. G. Snoek, “LiftPool: Bidirectional convnet pooling,” in ICLR , 2021
2021
Closest in time.
B. Fernando and S. Herath, “Anticipating human actions by correlating past with the future with Jaccard similarity measures,” in CVPR , 2021, pp. 13 224–13 233
2021
Closest in time.
L. Lu, W. Li, X. Tao, J. Lu, and J. Jia, “MASA-SR: Matching acceleration and spatial adaptation for reference-based image super-resolution,” in CVPR , 2021, pp. 6368–6377
2021
Closest in time.
A. Stergiou and R. Poppe, “Learn to cycle: Time-consistent feature discovery for action recognition,” PRL , vol. 141, pp. 1–7, 2021
2021
Closest in time.
A. Stergiou and R. Poppe, “Multi-temporal convolutions for human action recognition in videos,” in IJCNN , 2021
2021
Closest in time.
H. Sim, J. Oh, and M. Kim, “XVFI: extreme video frame interpolation,” in ICCV , 2021
2021
Closest in time.
T. Ding, L. Liang, Z. Zhu, and I. Zharkov, “CDFI: Compression-driven network design for frame interpolation,” in CVPR , 2021, pp. 8001–8011
2021
Closest in time.
Y. Li, C.-Y. Wu, H. Fan, K. Mangalam, B. Xiong, J. Malik, and C. Feichtenhofer, “Mvitv2: Improved multiscale vision transformers for classification and detection,” in CVPR , 2022
2022
Closest in time.