Papers › Cross-Modal Self-Attention Network for Referring Image Segmentation
Cross-Modal Self-Attention Network for Referring Image Segmentation
Linwei Ye, Mrigank Rochan, Zhi Liu, Yang Wang
We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their representations. They do not sufficiently capture long-range correlations between these two modalities. In this paper, we propose a cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the input image. In addition, we propose a gated multi-level fusion module to selectively integrate self-attentive cross-modal features corresponding to different levels in the image. This module controls the information flow of features at different levels. We validate the proposed approach on four evaluation datasets. Our proposed approach consistently outperforms existing state-of-the-art methods.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Referring Expression Segmentation | RefCOCO+ test B | CMSA | Overall IoU | 37.89 | #27 of 30 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCOCO+ testA | CMSA | Overall IoU | 47.60 | #29 of 30 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCOCO+ val | CMSA | Overall IoU | 43.76 | #32 of 33 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCoCo val | CMSA | Overall IoU | 58.32 | #34 of 37 | Archive leaderboard | report |
| Referring Video Object Segmentation | Refer-YouTube-VOS | CMSA | F | 38.1 | #18 of 18 | Archive leaderboard | report |
| Referring Video Object Segmentation | Refer-YouTube-VOS | CMSA | J | 34.8 | #18 of 18 | Archive leaderboard | report |
| Referring Video Object Segmentation | Refer-YouTube-VOS | CMSA | J&F | 36.4 | #18 of 18 | 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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