arXiv:2412.01558v2 Announce Type: replace 
Abstract: Prevailing joint prediction transformers for Video Highlight Detection and Moment Retrieval (HD/MR) exhibit deficiencies in handling cross-task dynamics, achieving robust video-text alignment, and utilizing effective attention mechanisms, with the potential of Large Language/Vision-Language Models (LLMs/LVLMs) being largely untapped. This paper introduces VideoLights, a novel HD/MR framework addressing these limitations by incorporating: (i) Convolutional Projection and Feature Refinement modules with an alignment loss for enhanced video-text feature congruity; (ii) a Bi-Directional Cross-Modal Fusion network for strongly coupled query-aware representations; (iii) a Uni-directional joint-task feedback mechanism for synergistic task improvement; (iv) hard positive/negative losses for adaptive learning; and (v) the leveraging of LVLMs (e.g., BLIP-2) for superior multimodal feature integration and intelligent pre-training with synthetic data. Comprehensive evaluations on QVHighlights, TVSum, and Charades-STA benchmarks demonstrate that VideoLights significantly surpasses existing baselines, establishing new state-of-the-art performances. Codes and model checkpoints are available at https://github.com/dpaul06/VideoLights .

يمثل تقديم VideoLights تقدمًا كبيرًا في الكشف عن أبرز لحظات الفيديو واسترجاع اللحظات، حيث يتناول القيود الرئيسية في المحولات الحالية المتعلقة بالديناميات بين المهام ومواءمة الفيديو والنص. يتضمن هذا الإطار وحدات وآليات مبتكرة، بما في ذلك الإسقاط التلافيفي وتنقيح الميزات، لتحسين توافق الميزات وتعزيز تآزر المهام.

La introducción de VideoLights marca un avance significativo en la detección de momentos destacados en video y la recuperación de momentos, abordando limitaciones clave en los transformadores existentes relacionadas con la dinámica entre tareas y la alineación video-texto. Este marco incorpora módulos y mecanismos innovadores, incluyendo proyección convolucional y refinamiento de características, para mejorar la congruencia de características y la sinergia de tareas.

L'introduction de VideoLights représente une avancée significative dans la détection des moments forts vidéo et la récupération de moments, en abordant les principales limitations des transformateurs existants liées à la dynamique inter-tâches et à l'alignement vidéo-texte. Ce cadre intègre des modules et mécanismes innovants, y compris la projection convolutionnelle et le raffinement des caractéristiques, pour améliorer la congruence des caractéristiques et améliorer la synergie des tâches.

The introduction of VideoLights marks a significant advancement in joint video highlight detection and moment retrieval, addressing key limitations in existing transformers related to cross-task dynamics and video-text alignment. This framework incorporates innovative modules and mechanisms, including Convolutional Projection and Feature Refinement, to enhance feature congruity and improve task synergy.

VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval

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