Summary of Longvale: Vision-audio-language-event Benchmark Towards Time-aware Omni-modal Perception Of Long Videos, by Tiantian Geng et al.
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos
by Tiantian Geng, Jinrui Zhang, Qingni Wang, Teng Wang, Jinming Duan, Feng Zheng
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 paper proposes a novel pipeline for automatic video understanding, tackling the challenge of processing omni-modal videos with multiple events. The authors introduce LongVALE, a benchmark dataset comprising 105K events from 8.4K high-quality long videos, along with detailed captions and precise temporal boundaries. They also develop a baseline model that leverages LongVALE to enable large language models for fine-grained video understanding. The proposed pipeline consists of filtering, event boundary detection, and cross-modal correlation-aware captioning. Experimental results demonstrate the effectiveness of LongVALE in advancing multi-modal video understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way for computers to understand videos with many different types of information like pictures, sounds, and spoken words. It’s hard to make machines understand these kinds of videos because they’re very complex. To solve this problem, the authors developed a system that can automatically filter out bad parts of the video, find where one event ends and another begins, and write captions that describe what’s happening in the video. They also created a special dataset with many examples of videos like this, along with descriptions that explain what’s happening in each scene. This will help machines get better at understanding these kinds of videos. |
Keywords
» Artificial intelligence » Multi modal
