Papers › Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed...
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval
Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang, Gang Xiong, Yue Hu, Qi Wu
Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/context-i2w.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Composed Image Retrieval (ZS-CIR) | CIRCO | Context-I2W | mAP@10 | 14.62 | #31 of 43 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | CIRR | Context-I2W (CLIP L/14) | R@5 | 55.1 | #38 of 47 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | COCO (Common Objects in Context) | Context-I2W | Actions Recall@5 | 28.5 | #5 of 10 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | Fashion IQ | Context-I2W (CLIP L/14) | (Recall@10+Recall@50)/2 | 38.35 | #29 of 41 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | GeneCIS | Context-I2W (CLIP L/14) | A-R@1 | 12.7 | #7 of 11 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | ImageNet | Context-I2W | Average Recall | 20.25 | #6 of 11 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | ImageNet-R | Context-I2W | (Recall@10+Recall@50)/2 | 20.25 | #9 of 20 | 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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