Papers › Making a Case for 3D Convolutions for Object Segmentation in Videos

Making a Case for 3D Convolutions for Object Segmentation in Videos

26 Aug 2020arXiv:2008.11516archive 2025-07-28

Sabarinath Mahadevan, Ali Athar, Aljoša Ošep, Sebastian Hennen, Laura Leal-Taixé, Bastian Leibe

The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. We have made our code and trained models publicly available at https://github.com/sabarim/3DC-Seg.

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conv3x3x3 sabarim/3DC-Seg/network/Resnet3d.py official repository ran · our draft was wrong MIT (permissive) · c3b215555357b9d1 · report
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Tasks

DecoderSegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo ClassificationVideo Object SegmentationVideo PredictionVideo SegmentationVideo Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Video Object Segmentation DAVIS 2016 val 3DC-Seg F 84.7 #15 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val 3DC-Seg G 84.5 #15 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val 3DC-Seg J 84.3 #15 of 25 Archive leaderboard report
Video Object Segmentation DAVIS 2016 3DC-Seg F-Score 84.7 #18 of 24 Archive leaderboard report
Video Object Segmentation DAVIS 2016 3DC-Seg Jaccard (Mean) 84.3 #18 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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