DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks

TL;DR

DetailCLIP is a novel vision-language model that enhances fine-grained and detail-oriented tasks like segmentation by employing patch-level comparison, self-distillation, and an attention-based token removal mechanism to focus on critical image regions.

Contribution

The paper introduces a new framework, DetailCLIP, that improves fine-grained visual understanding by integrating patch comparison, pixel-level reconstruction, and attention mechanisms, surpassing existing models.

Findings

Outperforms existing CLIP-based models in segmentation accuracy

Demonstrates superior generalization across diverse datasets

Effectively captures detailed visual features for fine-grained tasks

Abstract

In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models, particularly CLIP, in handling detail-oriented and fine-grained tasks like segmentation. While CLIP and its variants excel in the global alignment of image and text representations, they often struggle to capture the fine-grained details necessary for precise segmentation. To overcome these challenges, we propose a novel framework that employs patch-level comparison of self-distillation and pixel-level reconstruction losses, enhanced with an attention-based token removal mechanism. This approach selectively retains semantically relevant tokens, enabling the model to focus on the image's critical regions aligned with the specific functions of our model, including textual information processing, patch comparison, and image reconstruction, ensuring that the model learns high-level semantics and detailed visual features. Our experiments demonstrate that DetailCLIP surpasses existing CLIP-based and traditional self-supervised learning (SSL) models in segmentation accuracy and exhibits superior generalization across diverse datasets. DetailCLIP represents a significant advancement in vision-language modeling, offering a robust solution for tasks that demand high-level semantic understanding and detailed feature extraction. https://github.com/KishoreP1/DetailCLIP.