VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval
Dhiman Paul, Md Rizwan Parvez, Nabeel Mohammed, Shafin Rahman
2024-12-04
Summary
This paper introduces VideoLights, a new framework designed to improve video highlight detection and moment retrieval by better aligning video and text information.
What's the problem?
Video highlight detection (HD) and moment retrieval (MR) are important tasks for analyzing videos, but many existing models do not effectively connect the visual content of videos with the textual descriptions. They often use simple attention mechanisms that fail to capture the complex relationships between video and text, leading to poor performance in identifying key moments in videos.
What's the solution?
To solve this problem, the researchers developed VideoLights, which includes several innovative features: it uses Convolutional Projection and Feature Refinement modules to improve the alignment between video and text, a Bi-Directional Cross-Modal Fusion network to create better representations of video clips based on user queries, and a Uni-directional joint-task feedback mechanism that enhances both highlight detection and moment retrieval. Additionally, they introduced hard positive/negative losses for better learning and utilized large vision-language models (LVLMs) like BLIP-2 to integrate features more effectively. These improvements allow the system to perform better on various benchmarks.
Why it matters?
This research is significant because it enhances how AI systems can analyze and understand videos, making it easier to find important moments and highlights based on user queries. This can be applied in many areas such as video editing, content creation, and social media, where quickly identifying key moments in videos is increasingly important as video content continues to grow.
Abstract
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 .