Summary of Localizing Events in Videos with Multimodal Queries, by Gengyuan Zhang and Mang Ling Ada Fok and Jialu Ma and Yan Xia and Daniel Cremers and Philip Torr and Volker Tresp and Jindong Gu
Localizing Events in Videos with Multimodal Queries
by Gengyuan Zhang, Mang Ling Ada Fok, Jialu Ma, Yan Xia, Daniel Cremers, Philip Torr, Volker Tresp, Jindong Gu
First submitted to arxiv on: 14 Jun 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 The paper proposes a new benchmark for localizing events in videos using multimodal queries (MQs) that integrate images to represent semantic queries. Current research primarily relies on natural language queries, overlooking the potential of MQs for non-verbal or unfamiliar concepts. The authors introduce ICQ, a benchmark designed for video event localization with MQs, and an evaluation dataset ICQ-Highlight. They also propose three Multimodal Query Adaptation methods and a Surrogate Fine-tuning strategy to evaluate existing video localization models. The paper benchmarks 12 state-of-the-art backbone models across diverse application domains, highlighting the potential of MQs in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using pictures to help find events in videos. Currently, most research uses words, but this doesn’t work well for things that can’t be described with words. The authors created a new way to test how good models are at finding events in videos when given a picture and some text. They also came up with ways to make existing models better at this task. This is important because it could help with things like searching for videos of specific actions or objects. |
Keywords
» Artificial intelligence » Fine tuning
