SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge
Not on the map. Why?
Abstract (arXiv)
Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction "Anna confronts Mark" is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text embeddings against each visual shot embedding of a video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 24 Sep 2026.
Builds on
18 of 24 references.
References · 18
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
- 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
- 2017Dense-Captioning Events in Videoscited by 789
- 2021CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrievalcited by 645
- 2021FILIP: Fine-grained Interactive Language-Image Pre-Trainingcited by 626
- 2022When and why vision-language models behave like bags-of-words, and what to do about it?cited by 603
- 2022X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrievalcited by 279
- 2018A Joint Sequence Fusion Model for Video Question Answering and Retrievalcited by 208
- 2020MovieNet: A Holistic Dataset for Movie Understandingcited by 206
- 2016Movie Descriptioncited by 180
- 2022X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrievalcited by 127
6 more
- 2021MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptionscited by 107
- 2022TS2-Net: Token Shift and Selection Transformer for Text-Video Retrievalcited by 104
- 2020Condensed Movies: Story Based Retrieval with Contextual Embeddingscited by 95
- 2022Disentangled Representation Learning for Text-Video Retrievalcited by 77
- 2022VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignmentcited by 20
- 2025Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrievalcited by 20
Built on by
Not cited yet.