SmartCLIP: Modular Vision-language Alignment with Identification Guarantees

2025-07-29

AI Summary

Contrastive vision-language models (e.g., CLIP) suffer from semantic misalignment—where image regions mismatch short caption semantics—and representation entanglement—where long-text alignment couples visual features—thereby limiting generalization in short-prompt downstream tasks. To address this, we propose a modular vision-language alignment framework grounded in a theoretically derived identifiability condition that ensures lossless cross-modal semantic preservation and visual representation disentanglement. Our approach introduces fine-grained text concept identification, dynamic image-text fragment alignment, and disentangled representation learning to enable multi-granularity semantic matching. Extensive experiments demonstrate significant improvements over strong baselines across diverse downstream tasks—including zero-shot classification, referring expression comprehension, and open-vocabulary detection—while effectively mitigating semantic misalignment. Ablation studies validate the theoretical soundness of our identifiability condition and confirm the model’s robust generalization capability under short-prompt settings.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Contrastive Language-Image Pre-training (CLIP)~citep{radford2021learning} has emerged as a pivotal model in computer vision and multimodal learning, achieving state-of-the-art performance at aligning visual and textual representations through contrastive learning. However, CLIP struggles with potential information misalignment in many image-text datasets and suffers from entangled representation. On the one hand, short captions for a single image in datasets like MSCOCO may describe disjoint regions in the image, leaving the model uncertain about which visual features to retain or disregard. On the other hand, directly aligning long captions with images can lead to the retention of entangled details, preventing the model from learning disentangled, atomic concepts -- ultimately limiting its generalization on certain downstream tasks involving short prompts. In this paper, we establish theoretical conditions that enable flexible alignment between textual and visual representations across varying levels of granularity. Specifically, our framework ensures that a model can not only emph{preserve} cross-modal semantic information in its entirety but also emph{disentangle} visual representations to capture fine-grained textual concepts. Building on this foundation, we introduce ours, a novel approach that identifies and aligns the most relevant visual and textual representations in a modular manner. Superior performance across various tasks demonstrates its capability to handle information misalignment and supports our identification theory. The code is available at https://github.com/Mid-Push/SmartCLIP.

Problem

• Addresses information misalignment in image-text datasets
• Solves entangled visual-textual representation issues
• Ensures granular alignment of multimodal semantic information

Innovation

• Modular alignment of visual-textual representations
• Disentanglement of visual features for granular concepts
• Theoretical guarantees for cross-modal information preservation