SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

TL;DR

This paper introduces Correlative Self-Attention (CSA), a simple modification to CLIP's vision encoder that enables effective zero-shot semantic segmentation without retraining, significantly improving performance on multiple benchmarks.

Contribution

The paper proposes CSA, a novel self-attention mechanism that allows CLIP to perform dense prediction tasks like semantic segmentation with minimal changes and no additional training.

Findings

CSA achieves 38.2% average zero-shot mIoU on eight benchmarks.

Outperforms existing state-of-the-art methods with 33.9% mIoU.

Significantly better than vanilla CLIP's 14.1% mIoU.

Abstract

Recent advances in contrastive language-image pretraining (CLIP) have demonstrated strong capabilities in zero-shot classification by aligning visual representations with target text embeddings in an image level. However, in dense prediction tasks, CLIP often struggles to localize visual features within an image and fails to give accurate pixel-level predictions, which prevents it from functioning as a generalized visual foundation model. In this work, we aim to enhance CLIP's potential for semantic segmentation with minimal modifications to its pretrained models. By rethinking self-attention, we surprisingly find that CLIP can adapt to dense prediction tasks by simply introducing a novel Correlative Self-Attention (CSA) mechanism. Specifically, we replace the traditional self-attention block of CLIP vision encoder's last layer by our CSA module and reuse its pretrained projection matrices of query, key, and value, leading to a training-free adaptation approach for CLIP's zero-shot semantic segmentation. Extensive experiments show the advantage of CSA: we obtain a 38.2% average zero-shot mIoU across eight semantic segmentation benchmarks highlighted in this paper, significantly outperforming the existing SoTA's 33.9% and the vanilla CLIP's 14.1%.