VideoLights: Feature Refinement and Cross-Task Alignment Transformer for
  Joint Video Highlight Detection and Moment Retrieval
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 .
674fe2394afd1cf077d45426
Dhiman Paul
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2024-12-04T05:23:35.288Z
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Dhiman Paul
user
dpaul06
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Md Rizwan Parvez
claimed_verified
2024-12-05T10:20:31.566Z
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Md Rizwan Parvez
user
mparvez
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Nabeel Mohammed
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Shafin Rahman
extracted_pending
2024-12-04T05:01:46.682Z
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Shafin Rahman
user
shafin5
VideoLights: Feature Refinement and Cross-Task Alignment Transformer for
  Joint Video Highlight Detection and Moment Retrieval
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 .
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.
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