Summary of Contextdet: Temporal Action Detection with Adaptive Context Aggregation, by Ning Wang et al.
ContextDet: Temporal Action Detection with Adaptive Context Aggregation
by Ning Wang, Yun Xiao, Xiaopeng Peng, Xiaojun Chang, Xuanhong Wang, Dingyi Fang
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces a novel approach to temporal action detection (TAD), a challenging task in video understanding. Existing methods struggle with variable segment lengths and ambiguous boundaries. The proposed ContextDet framework employs large-kernel convolutions for the first time in TAD, featuring a pyramid adaptive context aggregation architecture. This architecture consists of two modules: context attention module (CAM) and long context module (LCM). CAM identifies salient contextual information and preserves context integrity through a context gating block (CGB), while LCM gathers long-range context and fine-grained local features using a mixture of large- and small-kernel convolutions. The model is evaluated on six TAD benchmarks, including MultiThumos, Charades, FineAction, EPIC-Kitchens 100, Thumos14, and HACS, demonstrating superior accuracy at reduced inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in understanding videos called temporal action detection. It’s hard because actions can be short or long, and it’s tricky to figure out when they start and stop. The new approach uses something called “large-kernel convolutions” that helps the computer learn more about what’s happening in the video. This makes it better at recognizing and understanding actions. The new method is tested on lots of different videos and does a great job, but also runs faster than other methods. |
Keywords
» Artificial intelligence » Attention » Inference
