Papers › Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
Mingmin Zhen, Shiwei Li, Lei Zhou, Jiaxiang Shang, Haoan Feng, Tian Fang, Long Quan
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn discriminative features (D-features) from the input images that reveal feature distribution from a global perspective. The D-features are then used to establish correspondence with all features of test image under conditional random field (CRF) formulation, which is leveraged to enforce consistency between pixels. The experiments verify that DFNet outperforms state-of-the-art methods by a large margin with a mean IoU score of 83.4% and ranks first on the DAVIS-2016 leaderboard while using much fewer parameters and achieving much more efficient performance in the inference phase. We further evaluate DFNet on the FBMS dataset and the video saliency dataset ViSal, reaching a new state-of-the-art. To further demonstrate the generalizability of our framework, DFNet is also applied to the image object co-segmentation task. We perform experiments on a challenging dataset PASCAL-VOC and observe the superiority of DFNet. The thorough experiments verify that DFNet is able to capture and mine the underlying relations of images and discover the common foreground objects.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Video Object Segmentation | DAVIS 2016 val | DFNet | F | 81.8 | #18 of 25 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2016 val | DFNet | G | 82.6 | #18 of 25 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2016 val | DFNet | J | 83.4 | #18 of 25 | Archive leaderboard | report |
| Video Object Segmentation | DAVIS 2016 | DFNet | F-Score | 81.8 | #19 of 24 | Archive leaderboard | report |
| Video Object Segmentation | DAVIS 2016 | Ours | Jaccard (Mean) | 83.4 | #20 of 24 | Archive leaderboard | report |
| Video Object Segmentation | FBMS | DFNet | F-Score | 82.3 | #1 of 2 | 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.
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