Sparse Attention for Dense Open-Vocabulary Prediction in CLIP
Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to every pair of tokens-even semantically irrelevant ones. While these dense softmax weights are effective for gathering broad context during pre-training, they spread attention across many low-salience tokens, producing noise that obscures the fine-grained, spatially localized cues required for dense, open-vocabulary prediction. We study an inference-time substitution of the row-wise softmax in the final visual self-attention layers with the $α$-entmax transform, applied across both the standard query-key attention and self-correlation vari...
| Authors | Fatimah Zohra, Chen Zhao, Shuming Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 13 July 2026 |
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