Summary of Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models, by Fares Abawi and Di Fu and Stefan Wermter
Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models
by Fares Abawi, Di Fu, Stefan Wermter
First submitted to arxiv on: 5 May 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 This paper addresses the limitations of previous scanpath prediction models by developing a deep learning-based social cue integration model for saliency prediction. The proposed model integrates fixation history and social cues through a gating mechanism and sequential attention, allowing it to predict diverse individual scanpaths in videos. The authors evaluate their approach on gaze datasets of dynamic social scenes, observing that the late neural integration approach outperforms early fusion when training models on large datasets. The results also show that a single unified model trained on all observers’ scanpaths performs similarly or better than individually trained models, suggesting that the group saliency representations instill universal attention in the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making robots more like humans. Robots often follow patterns to mimic human behavior, but people’s eye movements are different and changing. The researchers created a new way for robots to understand people’s gaze by combining information from past eye movements and social cues. They tested their method on videos of people watching dynamic scenes and found that it works better than previous methods when trained on large amounts of data. This is important because robots will be able to interact more naturally with people, making human-robot interactions more natural and effective. |
Keywords
» Artificial intelligence » Attention » Deep learning
