Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval

• URL: http://arxiv.org/abs/2401.13329v3
• Date: Fri, 21 Feb 2025 12:30:11 GMT
• Title: Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval
• Authors: Dezhao Luo, Shaogang Gong, Jiabo Huang, Hailin Jin, Yang Liu,
• Abstract summary: Video moment retrieval (VMR) aims to locate the most likely video moment corresponding to a text query in untrimmed videos.<n>Training of existing methods is limited by the lack of diverse and generalisable VMR datasets.<n>We propose a Fine-grained Video Editing framework, termed FVE, that explores generative video diffusion.
• Score: 54.22321767540878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video moment retrieval (VMR) aims to locate the most likely video moment(s) corresponding to a text query in untrimmed videos. Training of existing methods is limited by the lack of diverse and generalisable VMR datasets, hindering their ability to generalise moment-text associations to queries containing novel semantic concepts (unseen both visually and textually in a training source domain). For model generalisation to novel semantics, existing methods rely heavily on assuming to have access to both video and text sentence pairs from a target domain in addition to the source domain pair-wise training data. This is neither practical nor scalable. In this work, we introduce a more generalisable approach by assuming only text sentences describing new semantics are available in model training without having seen any videos from a target domain. To that end, we propose a Fine-grained Video Editing framework, termed FVE, that explores generative video diffusion to facilitate fine-grained video editing from the seen source concepts to the unseen target sentences consisting of new concepts. This enables generative hypotheses of unseen video moments corresponding to the novel concepts in the target domain. This fine-grained generative video diffusion retains the original video structure and subject specifics from the source domain while introducing semantic distinctions of unseen novel vocabularies in the target domain. A critical challenge is how to enable this generative fine-grained diffusion process to be meaningful in optimising VMR, more than just synthesising visually pleasing videos. We solve this problem by introducing a hybrid selection mechanism that integrates three quantitative metrics to selectively incorporate synthetic video moments (novel video hypotheses) as enlarged additions to the original source training data, whilst minimising potential ...

Related papers

• REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing [56.992916488077476]
  In this work, we explore novel video editing models for generating shorts that feature a coherent narrative with embedded video insertions extracted from a long input video.<n>We propose a novel retrieval-embedded generation framework that allows a large language model to quote multimodal resources while maintaining a coherent narrative.<n>Our objective evaluations show that the proposed method can effectively insert short video clips while maintaining a coherent narrative.
  arXiv  Detail & Related papers  (2025-05-24T21:36:49Z)
• VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement [63.4357918830628]
  VideoRepair is a model-agnostic, training-free video refinement framework. It identifies fine-grained text-video misalignments and generates explicit spatial and textual feedback. VideoRepair substantially outperforms recent baselines across various text-video alignment metrics.
  arXiv  Detail & Related papers  (2024-11-22T18:31:47Z)
• Realizing Video Summarization from the Path of Language-based Semantic Understanding [19.825666473712197]
  We propose a novel video summarization framework inspired by the Mixture of Experts (MoE) paradigm. Our approach integrates multiple VideoLLMs to generate comprehensive and coherent textual summaries.
  arXiv  Detail & Related papers  (2024-10-06T15:03:22Z)
• MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval [53.417646562344906]
  Video Moment Retrieval (VMR) aims to localize a specific temporal segment within an untrimmed long video given a natural language query. Existing methods often suffer from inadequate training annotations, i.e., the sentence typically matches with a fraction of the prominent video content in the foreground with limited wording diversity. This intrinsic modality imbalance leaves a considerable portion of visual information remaining unaligned with text. In this work, we take an MLLM as a video narrator to generate plausible textual descriptions of the video, thereby mitigating the modality imbalance and boosting the temporal localization.
  arXiv  Detail & Related papers  (2024-06-25T18:39:43Z)
• Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels [34.88705952395676]
  Video moment retrieval (VMR) is to search for a visual temporal moment in an untrimmed raw video by a given text query description (sentence) We introduce a new approach called hybrid-learning video moment retrieval to solve the problem by knowledge transfer. Our aim is to explore shared universal knowledge between the two domains in order to improve model learning in the weakly-labelled target domain.
  arXiv  Detail & Related papers  (2024-06-03T21:14:53Z)
• StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation [117.13475564834458]
  We propose a new way of self-attention calculation, termed Consistent Self-Attention. To extend our method to long-range video generation, we introduce a novel semantic space temporal motion prediction module. By merging these two novel components, our framework, referred to as StoryDiffusion, can describe a text-based story with consistent images or videos.
  arXiv  Detail & Related papers  (2024-05-02T16:25:16Z)
• 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)
• Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation [74.51546366251753]
  Video topic segmentation unveils the coarse-grained semantic structure underlying videos. We introduce a multi-modal video topic segmenter that utilizes both video transcripts and frames. Our proposed solution significantly surpasses baseline methods in terms of both accuracy and transferability.
  arXiv  Detail & Related papers  (2023-11-30T21:59:05Z)
• Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation [92.55296042611886]
  We propose a framework called "Reuse and Diffuse" dubbed $textitVidRD$ to produce more frames following the frames already generated by an LDM. We also propose a set of strategies for composing video-text data that involve diverse content from multiple existing datasets.
  arXiv  Detail & Related papers  (2023-09-07T08:12:58Z)
• Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models [58.17315970207874]
  We propose a zero-shot method for adapting generalisable visual-textual priors from arbitrary VLM to facilitate moment-text alignment. Experiments conducted on three VMR benchmark datasets demonstrate the notable performance advantages of our zero-shot algorithm.
  arXiv  Detail & Related papers  (2023-09-01T13:06:50Z)
• Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation [93.18163456287164]
  This paper proposes a novel text-guided video-to-video translation framework to adapt image models to videos. Our framework achieves global style and local texture temporal consistency at a low cost.
  arXiv  Detail & Related papers  (2023-06-13T17:52:23Z)
• Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising [43.35391175319815]
  This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. We introduce a novel paradigm dubbed Gen-L-Video, capable of extending off-the-shelf short video diffusion models. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models.
  arXiv  Detail & Related papers  (2023-05-29T17:38:18Z)
• VideoXum: Cross-modal Visual and Textural Summarization of Videos [54.0985975755278]
  We propose a new joint video and text summarization task. The goal is to generate both a shortened video clip along with the corresponding textual summary from a long video. The generated shortened video clip and text narratives should be semantically well aligned.
  arXiv  Detail & Related papers  (2023-03-21T17:51:23Z)
• Domain Adaptive Video Segmentation via Temporal Consistency Regularization [32.77436219094282]
  This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) The first is cross-domain TCR that guides the prediction of target frames to have similar temporal consistency as that of source frames (learnt from annotated source data) via adversarial learning. The second is intra-domain TCR that guides unconfident predictions of target frames to have similar temporal consistency as confident predictions of target frames.
  arXiv  Detail & Related papers  (2021-07-23T02:50:42Z)
• Video Corpus Moment Retrieval with Contrastive Learning [56.249924768243375]
  Video corpus moment retrieval (VCMR) is to retrieve a temporal moment that semantically corresponds to a given text query. We propose a Retrieval and Localization Network with Contrastive Learning (ReLoCLNet) for VCMR. Experimental results show that ReLoCLNet encodes text and video separately for efficiency, its retrieval accuracy is comparable with baselines adopting cross-modal interaction learning.
  arXiv  Detail & Related papers  (2021-05-13T12:54:39Z)
• Open-book Video Captioning with Retrieve-Copy-Generate Network [42.374461018847114]
  In this paper, we convert traditional video captioning task into a new paradigm, ie, Open-book Video Captioning. We propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively. Our framework coordinates the conventional retrieval-based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video.
  arXiv  Detail & Related papers  (2021-03-09T08:17:17Z)
• Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos [63.94898634140878]
  Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. We propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. We evaluate the proposed method on three challenging public benchmarks: Ac-tivityNet Captions, TACoS, and Charades-STA.
  arXiv  Detail & Related papers  (2020-08-06T04:09:03Z)

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.