# Denoise-then-Retrieve: Text-Conditioned Video Denoising for Video Moment Retrieval

arXiv: https://arxiv.org/abs/2508.11313 · HTML: https://mlsanity.com/paper/2508.11313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/1910.14303/) (2019)
- [Multilevel Language and Vision Integration for Text-to-Clip Retrieval](https://mlsanity.com/paper/1804.05113/) (2018)
- [Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding](https://mlsanity.com/paper/2403.09626/) (2024)
- [Boundary Proposal Network for Two-Stage Natural Language Video Localization](https://mlsanity.com/paper/2103.08109/) (2021)
- [MAC: Mining Activity Concepts for Language-based Temporal Localization](https://mlsanity.com/paper/1811.08925/) (2018)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [MomentDiff: Generative Video Moment Retrieval from Random to Real](https://mlsanity.com/paper/2307.02869/) (2023)
- [Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos](https://mlsanity.com/paper/2007.14164/) (2020)
- [Spectrum-guided Multi-granularity Referring Video Object Segmentation](https://mlsanity.com/paper/2307.13537/) (2023)
- [Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos](https://mlsanity.com/paper/2008.02448/) (2020)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval](https://mlsanity.com/paper/2407.15051/) (2024)
- [Relation-aware Video Reading Comprehension for Temporal Language Grounding](https://mlsanity.com/paper/2110.05717/) (2021)
- [Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval](https://mlsanity.com/paper/2312.12155/) (2023)
- [Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2404.09263/) (2024)
- [Region Aware Video Object Segmentation with Deep Motion Modeling](https://mlsanity.com/paper/2207.10258/) (2022)
- [Temporally Consistent Referring Video Object Segmentation with Hybrid Memory](https://mlsanity.com/paper/2403.19407/) (2024)
- [Context-Enhanced Video Moment Retrieval with Large Language Models](https://mlsanity.com/paper/2405.12540/) (2024)
- [Referring Human Pose and Mask Estimation in the Wild](https://mlsanity.com/paper/2410.20508/) (2024)

## Abstract (arXiv)

Current text-driven Video Moment Retrieval (VMR) methods encode all video clips, including irrelevant ones, disrupting multimodal alignment and hindering optimization. To this end, we propose a denoise-then-retrieve paradigm that explicitly filters text-irrelevant clips from videos and then retrieves the target moment using purified multimodal representations. Following this paradigm, we introduce the Denoise-then-Retrieve Network (DRNet), comprising Text-Conditioned Denoising (TCD) and Text-Reconstruction Feedback (TRF) modules. TCD integrates cross-attention and structured state space blocks to dynamically identify noisy clips and produce a noise mask to purify multimodal video representations. TRF further distills a single query embedding from purified video representations and aligns it with the text embedding, serving as auxiliary supervision for denoising during training. Finally, we perform conditional retrieval using text embeddings on purified video representations for accurate VMR. Experiments on Charades-STA and QVHighlights demonstrate that our approach surpasses state-of-the-art methods on all metrics. Furthermore, our denoise-then-retrieve paradigm is adaptable and can be seamlessly integrated into advanced VMR models to boost performance.