Summary of Semantically Guided Representation Learning For Action Anticipation, by Anxhelo Diko et al.
Semantically Guided Representation Learning For Action Anticipation
by Anxhelo Diko, Danilo Avola, Bardh Prenkaj, Federico Fontana, Luigi Cinque
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel framework called Semantically Guided Representation Learning (S-GEAR) for action anticipation, which learns visual action prototypes and leverages language models to structure their relationships, inducing semanticity. S-GEAR is tested on four action anticipation benchmarks, achieving improved results compared to previous works, with gains of +3.5, +2.7, and +3.5 absolute points on Top-1 Accuracy, as well as a gain of +0.8 on Top-5 Recall. The paper also explores the transfer of geometric associations between actions from language to visual prototypes, demonstrating the impact of action semantic interconnectivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about predicting what will happen next in a series of events based on past observations. The challenge is that we don’t always know exactly what will happen next because many things are connected and uncertain. Instead of trying to predict more visual information or timing, this study focuses on learning how different actions relate to each other semantically. They developed a new way to do this called S-GEAR, which uses language models to understand the relationships between actions. The results show that S-GEAR is better at predicting what will happen next than previous methods. |
Keywords
* Artificial intelligence * Recall * Representation learning
