A Closer Look at the Explainability of Contrastive Language-Image Pre-training

TL;DR

This paper critically examines CLIP's explainability issues, identifies causes related to architecture and features, and proposes CLIP Surgery, a method that enhances interpretability and extends CLIP's capabilities without additional training.

Contribution

The paper introduces CLIP Surgery, a novel architecture modification technique that improves CLIP's explainability and open-vocabulary performance without fine-tuning.

Findings

CLIP tends to focus on background regions in visualizations.

Noisy activations are caused by redundant features among categories.

CLIP Surgery significantly improves explainability and multimodal visualization.

Abstract

Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its explainability, which undermine its credibility and limit the capacity for related tasks. Specifically, we find that CLIP tends to focus on background regions rather than foregrounds, with noisy activations at irrelevant positions on the visualization results. These phenomena conflict with conventional explainability methods based on the class attention map (CAM), where the raw model can highlight the local foreground regions using global supervision without alignment. To address these problems, we take a closer look at its architecture and features. Based on thorough analyses, we find the raw self-attentions link to inconsistent semantic regions, resulting in the opposite visualization. Besides, the noisy activations are owing to redundant features among categories. Building on these insights, we propose the CLIP Surgery for reliable CAM, a method that allows surgery-like modifications to the inference architecture and features, without further fine-tuning as classical CAM methods. This approach significantly improves the explainability of CLIP, surpassing existing methods by large margins. Besides, it enables multimodal visualization and extends the capacity of raw CLIP on open-vocabulary tasks without extra alignment. The code is available at https://github.com/xmed-lab/CLIP_Surgery.