DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks

2024-09-10

arXiv.org

AI Summary

Existing vision-language models (e.g., CLIP) struggle to model fine-grained local details, resulting in insufficient region-level semantic alignment—particularly for tasks like image segmentation. To address this, we propose a detail-oriented vision-language co-learning framework that integrates patch-level self-distilled contrastive learning, pixel-level reconstruction, and attention-guided semantic token pruning into the CLIP architecture, enabling joint optimization of global semantics and local features. Self-distillation enhances discriminability of fine-grained representations; pixel-level reconstruction enforces low-level visual fidelity; and attention-driven token sparsification concentrates learning on salient semantic regions. Evaluated on multiple segmentation benchmarks, our method consistently outperforms CLIP variants and state-of-the-art self-supervised approaches, achieving superior accuracy, generalization, and robustness.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Enhances CLIP for fine-grained tasks like segmentation
• Improves detail capture with patch and pixel-level losses
• Selectively retains tokens for better semantic alignment

Innovation

• Patch-level comparison with self-distillation
• Pixel-level reconstruction losses enhancement
• Attention-based token removal mechanism