Summary of Videolights: Feature Refinement and Cross-task Alignment Transformer For Joint Video Highlight Detection and Moment Retrieval, by Dhiman Paul et al.
VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval
by Dhiman Paul, Md Rizwan Parvez, Nabeel Mohammed, Shafin Rahman
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel framework for Video Highlight Detection and Moment Retrieval (HD/MR) called VideoLights. The framework addresses limitations in current models by incorporating convolutional projection and feature refinement modules, bi-directional cross-modal fusion networks, and uni-directional joint-task feedback mechanisms. Additionally, the researchers introduce hard positive/negative losses for adaptive error penalization and leverage large-language and vision-language models (LLM/LVLMs) like BLIP-2 for enhanced multimodal feature integration and intelligent pretraining using synthetic data generated from LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoLights is a new way to analyze videos by finding important moments and linking them to text descriptions. Current video analysis models often ignore how different parts of the model work together, which makes them not as good at understanding the relationships between what’s happening in the video and what it says. This framework fixes that problem with three main components: a way to align video and text features, a way to combine those features for better representations, and a feedback mechanism to improve both tasks by looking at how they work together. |
Keywords
» Artificial intelligence » Pretraining » Synthetic data
