# Text-Video Multi-Grained Integration for Video Moment Montage

arXiv: https://arxiv.org/abs/2412.09276 · HTML: https://mlsanity.com/paper/2412.09276/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://mlsanity.com/paper/2211.01335/) (2022)
- [Finding Moments in Video Collections Using Natural Language](https://mlsanity.com/paper/1907.12763/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Lightweight Moment Retrieval System with Global Re-Ranking and Robust Adaptive Bidirectional Temporal Search](https://mlsanity.com/paper/2504.09298/) (2025)
- [When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection](https://mlsanity.com/paper/2606.04098/) (2026)
- [Atom: Efficient On-Device Video-Language Pipelines Through Modular Reuse](https://mlsanity.com/paper/2512.17108/) (2025)

## Abstract (arXiv)

The proliferation of online short video platforms has driven a surge in user demand for short video editing. However, manually selecting, cropping, and assembling raw footage into a coherent, high-quality video remains laborious and time-consuming. To accelerate this process, we focus on a user-friendly new task called Video Moment Montage (VMM), which aims to accurately locate the corresponding video segments based on a pre-provided narration text and then arrange these video clips to create a complete video that aligns with the corresponding descriptions. The challenge lies in extracting precise temporal segments while ensuring intra-sentence and inter-sentence context consistency, as a single script sentence may require trimming and assembling multiple video clips. To address this problem, we present a novel textitText-Video Multi-Grained Integration method (TV-MGI) that efficiently fuses text features from the script with both shot-level and frame-level video features, which enables the global and fine-grained alignment between the video content and the corresponding textual descriptions in the script. To facilitate further research in this area, we introduce the Multiple Sentences with Shots Dataset (MSSD), a large-scale dataset designed explicitly for the VMM task. We conduct extensive experiments on the MSSD dataset to demonstrate the effectiveness of our framework compared to baseline methods.