Papers › Actor and Action Video Segmentation from a Sentence

Actor and Action Video Segmentation from a Sentence

20 Mar 2018CVPR 2018 6arXiv:1803.07485archive 2025-07-28

Kirill Gavrilyuk, Amir Ghodrati, Zhenyang Li, Cees G. M. Snoek

This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.

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JerryX1110/awesome-rvos mentioned on GitHubMIT report

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Tasks

Action SegmentationDecoderReferring Expression SegmentationSegmentationSentenceVideo SegmentationVideo Semantic Segmentation

Datasets

Introduced by this paper, per the archive.

A2D Sentences

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) AP 0.215 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) IoU mean 0.426 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) IoU overall 0.551 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) Precision@0.5 0.5 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) Precision@0.6 0.376 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) Precision@0.7 0.231 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) Precision@0.8 0.094 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. (Optical flow) Precision@0.9 0.004 #19 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. AP 0.198 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. IoU mean 0.421 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. IoU overall 0.536 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. Precision@0.5 0.475 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. Precision@0.6 0.347 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. Precision@0.7 0.211 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. Precision@0.8 0.08 #20 of 27 Archive leaderboard report
Referring Expression Segmentation A2D Sentences Gavriluyk el al. Precision@0.9 0.002 #20 of 27 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) AP 0.267 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) IoU mean 0.570 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) IoU overall 0.555 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) Precision@0.5 0.712 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) Precision@0.6 0.518 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) Precision@0.7 0.264 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) Precision@0.8 0.030 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. (Optical flow) Precision@0.9 0.000 #13 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. AP 0.233 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. IoU mean 0.542 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. IoU overall 0.541 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. Precision@0.5 0.699 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. Precision@0.6 0.460 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. Precision@0.7 0.173 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. Precision@0.8 0.014 #15 of 21 Archive leaderboard report
Referring Expression Segmentation J-HMDB Gavrilyuk et al. Precision@0.9 0.000 #15 of 21 Archive leaderboard report

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