RankCLIP: Ranking-Consistent Language-Image Pretraining

2024-04-15

arXiv.org

AI Summary

Existing vision-language contrastive learning models (e.g., CLIP) rely on strict one-to-one image–text matching, failing to capture complex many-to-many semantic relationships between modalities. To address this, we propose RankCLIP, a self-supervised contrastive pretraining framework grounded in ranking consistency. It introduces, for the first time, a list-wise contrastive loss that jointly optimizes intra-modal and cross-modal ranking consistency, thereby departing from conventional pairwise matching paradigms. RankCLIP requires no human annotations and achieves fine-grained alignment through multi-granularity semantic ranking. Evaluated on zero-shot image classification and other downstream tasks, RankCLIP consistently outperforms CLIP and its leading variants. These results demonstrate that modeling ranking consistency significantly enhances cross-modal semantic alignment capability—yielding both improved effectiveness and strong generalization across diverse vision-language understanding tasks.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. To this end, we introduce RANKCLIP, a novel pretraining method that extends beyond the rigid one-to-one matching framework of CLIP and its variants. By extending the traditional pair-wise loss to list-wise, and leveraging both in-modal and cross-modal ranking consistency, RANKCLIP improves the alignment process, enabling it to capture the nuanced many-to-many relationships between and within each modality. Through comprehensive experiments, we demonstrate the effectiveness of RANKCLIP in various downstream tasks, notably achieving significant gains in zero-shot classifications over state-of-the-art methods, underscoring the importance of this enhanced learning process.

Problem

• Extends CLIP to handle many-to-many text-image relationships
• Improves alignment via in-modal and cross-modal ranking consistency
• Enhances zero-shot classification performance over current methods

Innovation

• Extends CLIP with list-wise loss
• Leverages in-modal ranking consistency
• Improves cross-modal many-to-many alignment