SmartCLIP: Modular Vision-language Alignment with Identification Guarantees

TL;DR

SmartCLIP introduces a modular vision-language alignment framework with theoretical guarantees, effectively handling information misalignment and enabling disentangled, fine-grained representations for improved multimodal understanding.

Contribution

It provides a theoretical foundation for flexible alignment at different granularities and proposes a modular approach, SmartCLIP, with proven effectiveness and available code.

Findings

Outperforms existing models on various tasks

Handles information misalignment effectively

Supports disentangled, fine-grained representations

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.