Summary of Interpretable Action Recognition on Hard to Classify Actions, by Anastasia Anichenko and Frank Guerin and Andrew Gilbert
Interpretable Action Recognition on Hard to Classify Actions
by Anastasia Anichenko, Frank Guerin, Andrew Gilbert
First submitted to arxiv on: 19 Sep 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 In this paper, researchers develop an interpretable model for understanding videos by recognizing complex activities involving spatial and temporal relationships between objects and parts. Inspired by human video understanding, the model uses object positions, hand motions, and other features to recognize activities like objects entering containers. The team identifies three classes where the model struggled due to a lack of 3D information and addresses this limitation by incorporating state-of-the-art object detection and depth estimation models. These extensions are evaluated on the Something-Something-v2 dataset, showing significant performance improvements with the addition of depth relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to understand videos like humans do. It’s hard for machines to figure out what’s happening in videos because they don’t see things the same way we do. The researchers wanted to make their video understanding model more human-like, so they added features that help it recognize objects and their movements in 3D space. This helped the model get better at recognizing certain actions in a dataset called Something-Something-v2. |
Keywords
» Artificial intelligence » Depth estimation » Object detection
