title: VideoLights: Feature Refinement and Cross-Task Alignment Transformer for
    Joint Video Highlight Detection and Moment Retrieval
summary: Video Highlight Detection and Moment Retrieval (HD/MR) are essential in video
  analysis. Recent joint prediction transformer models often overlook their
  cross-task dynamics and video-text alignment and refinement. Moreover, most
  models typically use limited, uni-directional attention mechanisms, resulting
  in weakly integrated representations and suboptimal performance in capturing
  the interdependence between video and text modalities. Although large-language
  and vision-language models (LLM/LVLMs) have gained prominence across various
  domains, their application in this field remains relatively underexplored. Here
  we propose VideoLights, a novel HD/MR framework addressing these limitations
  through (i) Convolutional Projection and Feature Refinement modules with an
  alignment loss for better video-text feature alignment, (ii) Bi-Directional
  Cross-Modal Fusion network for strongly coupled query-aware clip
  representations, and (iii) Uni-directional joint-task feedback mechanism
  enhancing both tasks through correlation. In addition, (iv) we introduce hard
  positive/negative losses for adaptive error penalization and improved learning,
  and (v) leverage LVLMs like BLIP-2 for enhanced multimodal feature integration
  and intelligent pretraining using synthetic data generated from LVLMs.
  Comprehensive experiments on QVHighlights, TVSum, and Charades-STA benchmarks
  demonstrate state-of-the-art performance. Codes and models are available at
  https://github.com/dpaul06/VideoLights .
paper_authors._id: 674fe2394afd1cf077d45426
paper_authors.name: Dhiman Paul
paper_authors.status: extracted_confirmed
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paper_authors.avatarUrl: /avatars/72b25518eb12d277cca4f45a51fd0675.svg
paper_authors.fullname: Dhiman Paul
paper_authors.type: user
paper_authors.user: dpaul06
paper_authors._id: 674fe2394afd1cf077d45427
paper_authors.name: Md Rizwan Parvez
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-05T10:20:31.566Z
paper_authors._id: 65ae1c4468139e3c42973fe4
paper_authors.avatarUrl: /avatars/b065a857dd763410caadea37a2dc01c4.svg
paper_authors.fullname: Md Rizwan Parvez
paper_authors.type: user
paper_authors.user: mparvez
paper_authors._id: 674fe2394afd1cf077d45428
paper_authors.name: Nabeel Mohammed
paper_authors._id: 674fe2394afd1cf077d45429
paper_authors.name: Shafin Rahman
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-12-04T05:01:46.682Z
paper_authors._id: 666d3b7d067382b3e9a4331d
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paper_authors.fullname: Shafin Rahman
paper_authors.type: user
paper_authors.user: shafin5
paper_title: VideoLights: Feature Refinement and Cross-Task Alignment Transformer for
    Joint Video Highlight Detection and Moment Retrieval
paper_summary: Video Highlight Detection and Moment Retrieval (HD/MR) are essential in video
  analysis. Recent joint prediction transformer models often overlook their
  cross-task dynamics and video-text alignment and refinement. Moreover, most
  models typically use limited, uni-directional attention mechanisms, resulting
  in weakly integrated representations and suboptimal performance in capturing
  the interdependence between video and text modalities. Although large-language
  and vision-language models (LLM/LVLMs) have gained prominence across various
  domains, their application in this field remains relatively underexplored. Here
  we propose VideoLights, a novel HD/MR framework addressing these limitations
  through (i) Convolutional Projection and Feature Refinement modules with an
  alignment loss for better video-text feature alignment, (ii) Bi-Directional
  Cross-Modal Fusion network for strongly coupled query-aware clip
  representations, and (iii) Uni-directional joint-task feedback mechanism
  enhancing both tasks through correlation. In addition, (iv) we introduce hard
  positive/negative losses for adaptive error penalization and improved learning,
  and (v) leverage LVLMs like BLIP-2 for enhanced multimodal feature integration
  and intelligent pretraining using synthetic data generated from LVLMs.
  Comprehensive experiments on QVHighlights, TVSum, and Charades-STA benchmarks
  demonstrate state-of-the-art performance. Codes and models are available at
  https://github.com/dpaul06/VideoLights .
paper_ai_summary: VideoLights is a framework for video highlight detection and moment retrieval that uses enhanced video-text alignment, bi-directional cross-modal fusion, and leverages large vision-language models to achieve state-of-the-art performance.
paper_ai_keywords: Convolutional Projection Feature Refinement alignment loss Bi-Directional Cross-Modal Fusion query-aware clip representations Uni-directional joint-task feedback mechanism hard positive/negative losses large vision-language models BLIP-2 multimodal feature integration