Papers › Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Jae Shin Yoon, Francois Rameau, Junsik Kim, Seokju Lee, Seunghak Shin, In So Kweon
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation. Two-stage training (pre-training and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence. Experiments on large datasets demonstrate the effectiveness of our model - against related methods - in terms of accuracy, speed, and stability. Finally, we introduce the transferability of our network to different domains, such as the infrared data domain.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | F-measure (Decay) | 14.7 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | F-measure (Mean) | 62.5 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | F-measure (Recall) | 73.2 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | J&F | 66.35 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | Jaccard (Decay) | 11.2 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | Jaccard (Mean) | 70.2 | #73 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | PLM | Jaccard (Recall) | 86.3 | #73 of 78 | 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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