by Researchers at MIT have made significant strides in enhancing the peripheral vision capabilities of artificial intelligence (AI) models. While humans possess the ability to see shapes and objects outside their direct line of sight with less detail, AI lacks this crucial feature. By equipping AI models with peripheral vision capabilities, researchers aim to improve their ability to detect approaching hazards and predict human behavior in various scenarios.
The MIT researchers developed an image dataset that enables them to simulate peripheral vision in machine learning models. Training models with this dataset resulted in improved object detection in the visual periphery, although the models still lagged behind human performance. Interestingly, factors like object size or visual clutter did not strongly influence the AI’s performance, highlighting a fundamental difference in how AI models perceive the world compared to humans.
Vasha DuTell, a postdoc and co-author of the study, emphasized the need to identify the missing elements in AI models to make them more human-like in their visual perception. Understanding peripheral vision in AI models could not only enhance driver safety but also lead to the development of user-friendly displays and better predictions of human behavior.
Lead author Anne Harrington MEng ’23 highlighted the potential of modeling peripheral vision in AI to uncover the features in a visual scene that drive eye movements and information gathering. The research team, including co-authors like Mark Hamilton and Ayush Tewari, aims to bridge the gap between human and AI visual perception by exploring new ways to model peripheral vision in machine learning algorithms.
The modified technique employed by the MIT researchers mirrors human peripheral vision more accurately by capturing the loss of detail that occurs when individuals look beyond their focal point. This approach, known as the texture tiling model, transforms images to replicate the visual information loss in the periphery without the need for prior knowledge of eye movements.
By training computer vision models with the generated dataset, the researchers observed notable performance improvements in object detection tasks. However, the AI models still fell short of human performance, particularly in detecting objects in the far periphery. The discrepancy in performance suggests that AI models may lack the contextual understanding essential for accurate object detection tasks.
Moving forward, the researchers aim to delve deeper into these differences to develop AI models that can predict human performance in peripheral vision tasks. This advancement could lead to AI systems that enhance driver safety by alerting individuals to potential hazards. The publicly available dataset created by the researchers is expected to inspire further research in computer vision and AI, fostering collaborations to advance the field.
Justin Gardner, an associate professor at Stanford University, praised the MIT study for shedding light on the complex nature of human peripheral vision and its optimization for real-world tasks. The findings underscore the need for continued AI research to draw insights from the neuroscience of human vision, with the dataset provided by the MIT researchers serving as a valuable resource for future studies.
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1. Source: Coherent Market Insights, Public sources, Desk research
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