Papers › CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzheng Ma, Xupeng Miao, Xuming He, Bin Cui
Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification. To further improve its downstream performance, existing works propose additional learnable modules upon CLIP and fine-tune them by few-shot training sets. However, the resulting extra training cost and data requirement severely hinder the efficiency for model deployment and knowledge transfer. In this paper, we introduce a free-lunch enhancement method, CALIP, to boost CLIP's zero-shot performance via a parameter-free Attention module. Specifically, we guide visual and textual representations to interact with each other and explore cross-modal informative features via attention. As the pre-training has largely reduced the embedding distances between two modalities, we discard all learnable parameters in the attention and bidirectionally update the multi-modal features, enabling the whole process to be parameter-free and training-free. In this way, the images are blended with textual-aware signals and the text representations become visual-guided for better adaptive zero-shot alignment. We evaluate CALIP on various benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot classification, showing consistent zero-shot performance improvement over CLIP. Based on that, we further insert a small number of linear layers in CALIP's attention module and verify our robustness under the few-shot settings, which also achieves leading performance compared to existing methods. Those extensive experiments demonstrate the superiority of our approach for efficient enhancement of CLIP.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Training-free 3D Point Cloud Classification | ModelNet40 | CALIP | Accuracy (%) | 21.5 | #6 of 7 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ModelNet40 | CALIP | Need 3D Data? | No | #6 of 7 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ScanObjectNN | CALIP | Accuracy (%) | 16.9 | #5 of 6 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ScanObjectNN | CALIP | Need 3D Data? | No | #5 of 6 | Archive leaderboard | report |
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