DPDV: Dual-Pathway and Dual-View Representation Learning for Bridging Information Asymmetry in Text-Video Retrieval

ACL ARR 2026 January Submission3336 Authors

Keywords: Text-Video Retrieval, Information Asymmetry, Dual-Pathway Partitioning, Dual-View Interaction

Abstract: In recent years, CLIP-based text-video retrieval methods have developed rapidly, with research focusing on constructing diverse features and achieving effective interactions. However, the asymmetry of cross-modal information poses a challenge to accurately establishing retrieval relationships. To overcome this challenge, we propose a novel video retrieval framework, termed the Dual-Pathway and Dual-View model (DPDV), which consists of the Dual-Pathway Partitioning Module (DPPM) for constructing features at an appropriate granularity and the Dual-View Interaction Module (DVIM) for performing effective feature interactions. For DPPM, we simulate a human macro-level cognitive perspective by partitioning visual features into two categories based on their relevance to the text query and supplementing less relevant features with additional textual information. For DVIM, we simulate a human alignment strategy from macro to micro levels, focusing on local visual features while comprehensively modeling fine-grained interactions. We evaluate DPDV on five benchmark datasets, achieving leading retrieval performance.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Multimodal Retrieval, Video-Text Alignment, Representation Learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 3336