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Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval

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arxiv 2503.19009 v1 pith:32R6UWOW submitted 2025-03-24 cs.CV cs.IR

Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval

Arun Reddy , Alexander Martin , Eugene Yang , Andrew Yates , Kate Sanders , Kenton Murray , Reno Kriz , Celso M. de Melo
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Benjamin Van Durme Rama Chellappa
This is my paper
classification cs.CV cs.IR
keywords interactionretrievaltext-to-videovideo-colbertfine-grainedlaterepresentationstraining
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos. Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. These representations lead to increases in performance on common text-to-video retrieval benchmarks compared to other bi-encoder methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

    cs.IR 2026-03 unverdicted novelty 5.0

    Coverage-focused retrieval metrics correlate strongly with nugget coverage in RAG responses across text and multimodal benchmarks, supporting their use as performance proxies when retrieval and generation goals align.

  2. Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

    cs.IR 2026-03 unverdicted novelty 5.0

    Coverage-based retrieval metrics strongly correlate with nugget coverage in RAG outputs at topic and system level, supporting retrieval metrics as proxies for RAG performance when objectives align.