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

• URL: http://arxiv.org/abs/2508.11313v1
• Date: Fri, 15 Aug 2025 08:34:05 GMT
• Title: Denoise-then-Retrieve: Text-Conditioned Video Denoising for Video Moment Retrieval
• Authors: Weijia Liu, Jiuxin Cao, Bo Miao, Zhiheng Fu, Xuelin Zhu, Jiawei Ge, Bo Liu, Mehwish Nasim, Ajmal Mian,
• Abstract summary: Current text-driven Video Moment Retrieval (VMR) methods encode all video clips, including irrelevant ones, disrupting multimodal alignment and hindering optimization.<n>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.
• Score: 21.98012334983341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PREGEN: Uncovering Latent Thoughts in Composed Video Retrieval [9.493866391853723]
  Composed Video Retrieval (CoVR) aims to retrieve a video based on a query video and a modifying text.<n>Current CoVR methods fail to fully exploit modern Vision-Language Models (VLMs)<n>We introduce PREGEN, an efficient and powerful CoVR framework that overcomes these limitations.
  arXiv  Detail & Related papers  (2026-01-20T09:57:04Z)
• Adaptive Begin-of-Video Tokens for Autoregressive Video Diffusion Models [11.913945404405865]
  Most video diffusion models (VDMs) generate videos in an autoregressive manner, generating subsequent iteration frames conditioned on previous ones.<n>We propose Adaptive Begin-of-Video Tokens (ada-BOV) for autoregressive VDMs.
  arXiv  Detail & Related papers  (2025-11-15T08:29:14Z)
• InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing [66.48064661467781]
  We introduce sparse-frame video dubbing, a novel paradigm that strategically preserves references to maintain identity, iconic gestures, and camera trajectories.<n>We propose InfiniteTalk, a streaming audio-driven generator designed for infinite-length long sequence dubbing.<n> Comprehensive evaluations on HDTF, CelebV-HQ, and EMTD datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-19T17:55:23Z)
• Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks [21.710127132217526]
  We introduce a new paradigm called Video Interface Networks (VINs), which augment DiTs with an abstraction module to enable parallel inference of video chunks.<n>VINs encode global semantics from the noisy input of local chunks and the encoded representations, in turn, guide DiTs in denoising chunks in parallel.<n>Our approach attains state-of-the-art motion smoothness while using 25-40% fewer FLOPs than full generation.
  arXiv  Detail & Related papers  (2025-03-21T21:13:02Z)
• CANeRV: Content Adaptive Neural Representation for Video Compression [89.35616046528624]
  We propose Content Adaptive Neural Representation for Video Compression (CANeRV)<n>CANeRV is an innovative INR-based video compression network that adaptively conducts structure optimisation based on the specific content of each video sequence.<n>We show that CANeRV can outperform both H.266/VVC and state-of-the-art INR-based video compression techniques across diverse video datasets.
  arXiv  Detail & Related papers  (2025-02-10T06:21:16Z)
• VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding [44.382937324454254]
  Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding. We propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus. With VERIFIED, we construct a more challenging fine-grained VCMR benchmark containing Charades-FIG, DiDeMo-FIG, and ActivityNet-FIG.
  arXiv  Detail & Related papers  (2024-10-11T07:42:36Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• MEVG: Multi-event Video Generation with Text-to-Video Models [18.06640097064693]
  We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a pre-trained text-to-video generative model without a fine-tuning process. Our proposed method is superior to other video-generative models in terms of temporal coherency of content and semantics.
  arXiv  Detail & Related papers  (2023-12-07T06:53:25Z)
• POS: A Prompts Optimization Suite for Augmenting Text-to-Video Generation [11.556147036111222]
  This paper aims to enhance the diffusion-based text-to-video generation by improving the two input prompts, including the noise and the text. We propose POS, a training-free Prompt Optimization Suite to boost text-to-video models.
  arXiv  Detail & Related papers  (2023-11-02T02:33:09Z)
• Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator [59.589919015669274]
  This study focuses on zero-shot text-to-video generation considering the data- and cost-efficient. We propose a novel Free-Bloom pipeline that harnesses large language models (LLMs) as the director to generate a semantic-coherence prompt sequence. We also propose a series of annotative modifications to adapting LDMs in the reverse process, including joint noise sampling, step-aware attention shift, and dual-path.
  arXiv  Detail & Related papers  (2023-09-25T19:42:16Z)
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
  Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
  arXiv  Detail & Related papers  (2023-05-23T09:03:19Z)
• Contrastive Video-Language Learning with Fine-grained Frame Sampling [54.542962813921214]
  FineCo is an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames. It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence.
  arXiv  Detail & Related papers  (2022-10-10T22:48:08Z)
• Restore from Restored: Video Restoration with Pseudo Clean Video [28.057705167363327]
  We propose a self-supervised video denoising method called "restore-from-restored" This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm.
  arXiv  Detail & Related papers  (2020-03-09T17:37:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.