Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report

This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Chenliang Xu,et al.  Action Understanding with Multiple Classes of Actors , 2017, ArXiv.

[3]  Chenliang Xu,et al.  Can humans fly? Action understanding with multiple classes of actors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Mauricio Zambrano-Bigiarini,et al.  Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  Christian Bauckhage,et al.  Who is doing what? Simultaneous recognition of actions and actors , 2012, 2012 19th IEEE International Conference on Image Processing.

[7]  Chenliang Xu,et al.  Weakly Supervised Actor-Action Segmentation via Robust Multi-task Ranking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[9]  A. Buja,et al.  Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications , 2005 .

[10]  Cordelia Schmid,et al.  Joint Learning of Object and Action Detectors , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[12]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[13]  Yu Liu,et al.  CNN-RNN: a large-scale hierarchical image classification framework , 2018, Multimedia Tools and Applications.

[14]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[15]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[16]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[18]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[19]  Xiaogang Wang,et al.  Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[21]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[22]  Yiming Yang,et al.  Deep Learning for Extreme Multi-label Text Classification , 2017, SIGIR.

[23]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[24]  Yutaka Satoh,et al.  Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[26]  Wei Chen,et al.  Action Detection by Implicit Intentional Motion Clustering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).