Summary of Mulcpred: Learning Multi-modal Concepts For Explainable Pedestrian Action Prediction, by Yan Feng et al.
MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
by Yan Feng, Alexander Carballo, Keisuke Fujii, Robin Karlsson, Ming Ding, Kazuya Takeda
First submitted to arxiv on: 14 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 A novel framework called MulCPred is proposed to predict pedestrian actions with explainable predictions. The current state-of-the-art methods lack transparency, making it challenging to trust their outputs. To address this limitation, MulCPred integrates multi-modal concepts represented by training samples using a linear aggregator, which provides ante-hoc explanations of the relevance between concepts and predictions. Additionally, a channel-wise recalibration module is employed to attend to local spatiotemporal regions, enabling concepts with locality. A feature regularization loss is also introduced to encourage diverse pattern learning. MulCPred is evaluated on multiple datasets and tasks, demonstrating promising results in improving explainability without compromising performance. Furthermore, the framework’s ability to remove unrecognizable concepts improves cross-dataset prediction performance, indicating potential for generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MulCPred is a new way to predict what people will do next when they’re walking or driving. Right now, these predictions aren’t very transparent, which makes it hard to trust them. The new framework tries to fix this by understanding how different things relate to each other. It uses special “concepts” that are like building blocks for making predictions. These concepts help explain why the model is making certain predictions. The framework also pays attention to small details in the input data, which helps it make more accurate predictions. MulCPred was tested on lots of different datasets and tasks, and it did a great job of making transparent predictions without sacrificing accuracy. |
Keywords
» Artificial intelligence » Attention » Multi modal » Regularization » Spatiotemporal
