Papers › CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
Seokju Cho, Heeseong Shin, Sunghwan Hong, Anurag Arnab, Paul Hongsuck Seo, Seungryong Kim
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work, we introduce a novel cost-based approach to adapt vision-language foundation models, notably CLIP, for the intricate task of semantic segmentation. Through aggregating the cosine similarity score, i.e., the cost volume between image and text embeddings, our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders, addressing the challenges faced by existing methods in handling unseen classes. Building upon this, we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore, we examine various methods for efficiently fine-tuning CLIP.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open Vocabulary Semantic Segmentation | ADE20K-150 | CAT-Seg | mIoU | 37.9 | #4 of 23 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | ADE20K-847 | CAT-Seg | mIoU | 16.0 | #4 of 19 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PASCAL Context-459 | CAT-Seg | mIoU | 23.8 | #4 of 15 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PASCAL Context-59 | CAT-Seg | mIoU | 63.3 | #3 of 24 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PascalVOC-20 | CAT-Seg | mIoU | 97.0 | #4 of 20 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PascalVOC-20b | CAT-Seg | mIoU | 82.5 | #2 of 4 | 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
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