Papers › CRIS: CLIP-Driven Referring Image Segmentation
CRIS: CLIP-Driven Referring Image Segmentation
Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu
Referring image segmentation aims to segment a referent via a natural linguistic expression.Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmentation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing. The code will be released.
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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 |
|---|---|---|---|---|---|---|---|
| Generalized Referring Expression Segmentation | gRefCOCO | CRIS | cIoU | 55.34 | #10 of 13 | Archive leaderboard | report |
| Generalized Referring Expression Segmentation | gRefCOCO | CRIS | gIoU | 56.27 | #10 of 13 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCOCO+ test B | CRIS | Overall IoU | 53.68 | #20 of 30 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCOCO+ testA | CRIS | Overall IoU | 68.08 | #20 of 30 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCOCO+ val | CRIS | Overall IoU | 62.27 | #22 of 33 | Archive leaderboard | report |
| Referring Expression Segmentation | RefCoCo val | CRIS | Overall IoU | 70.47 | #25 of 37 | 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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