CVPR2015 論文紹介
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Transcript of CVPR2015 論文紹介
CVPR2015 論文紹介
@jellied_unagi
今回紹介する論文
• 動作検出(Action detection / localization)
• 入力: 映像,出力: 特定動作を含む時空間oo
• 動作認識(Action recognition / classification)
• 入力: 映像,出力: 動作クラス
• 映像要約(Action summarization)
• 入力: (複数)映像,出力: 映像のうち重要な部分
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Finding Action Tubes Georgia Gkioxari, Jitendra Malik http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.html
Pipeline for generating action proposals (spatiotemporal volumes including actions)
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Linking detection results via DP:
Actionness for Rt, Rt+1 + their spatial overlap
- Action detection (bounding boxes per frame) by learning CNN-based features with SVM
- Outlier suppression based on motion saliency
Fast Action Proposals for Human Action Detection and Search Gang Yu and Junsong Yuan http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Yu_Fast_Action_Proposals_2015_CVPR_paper.html
Pipeline for generating action proposals (spatiotemporal paths including actions)
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- Human detection + dense trajectories to generateaction paths with an action-ness score
- Greedy sub-path search to find best path sets
Action-ness for box bt on path p(i)
Overlaps between paths (to avoid redundancy)
Overlaps between boxes (to ensure smoothness of paths)
Motion Part Regularization: Improving Action Recognition via Trajectory Group Selection Bingbing Ni, Pierre Moulin, Xiaokang Yang and Shuicheng Yan http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ni_Motion_Part_Regularization_2015_CVPR_paper.html
Action localization (finding important motion parts) via group sparse optimization
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Hierarchical clustering to generatetrajectory groups at various sizes
Finding discriminative trajectory groups viagroup lasso (sum of L2 regularizations)
Pooled Motion Features for First-Person Videos M. S. Ryoo, Brandon Rothrock, and Larry Matthies http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.html
Encoding temporal changes of features by various temporal pooling filters
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Delving into Egocentric Actions Yin Li, Zhefan Ye and James M. Rehg http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Li_Delving_Into_Egocentric_2015_CVPR_paper.html
- Studying effective features for action recognition
- Better combinations: Obj. + Mot. + Ego-cues + Feat. around gaze
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Modeling Video Evolution For Action Recognition Basura Fernando, Efstratios Gavves, Jose ́ Oramas M., Amir Ghodrati and Tinne Tuytelaars http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Fernando_Modeling_Video_Evolution_2015_CVPR_paper.html
Describe how videos change via learning-to-rank
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Order constraints allow non-linear scalings of temporal changes in actions
Gaze-enabled Egocentric Video Summarization via Constrained Submodular Maximization Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg and Vikas Singh http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Xu_Gaze-Enabled_Egocentric_Video_2015_CVPR_paper.html
Single video summarization by using gaze information
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Segmenting shots where saccades occur
Measuring importance of shotsbased on fixation counts
R-CNN around PoG(to measure shot similarities)
Evaluation: Asking human experts to generate summaries
by grouping those action annotations, and asking them to select 5 ∼ 15 group of
consequent segments (referred to as events or blocks)
Video Co-summarization: Video Summarization by Visual Co-occurrence Wen-Sheng Chu, Yale Song and Alejandro Jaimes http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Chu_Video_Co-Summarization_Video_2015_CVPR_paper.html
Summarizing videos in a similar way to co-segmentation
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- Describing similarities between sub-shots by a bipartite graph
- Shots in a query video x shots in relevant videos
- Maximum cliques in the graph describe relevant events
EgoSampling: Fast-Forward and Stereo for Egocentric Videos Yair Poleg, Tavi Halperin, Chetan Arora and Shmuel Peleg http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Poleg_EgoSampling_Fast-Forward_and_2015_CVPR_paper.html
Frame sampling strategy to stabilize and fast-forward first-person videos
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Finding a shortest path where selected frames have 1) FOE at their center 2) faster camera motions 3) similar appearances to their previous / next frames