Summary of Towards Two-stream Foveation-based Active Vision Learning, by Timur Ibrayev et al.
Towards Two-Stream Foveation-based Active Vision Learning
by Timur Ibrayev, Amitangshu Mukherjee, Sai Aparna Aketi, Kaushik Roy
First submitted to arxiv on: 24 Mar 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 The proposed machine learning framework draws inspiration from the “two-stream hypothesis” in neuroscience, which explains how the human visual cortex processes visual information. The framework models separate regions of the brain for answering “what” and “where” questions, comprising a ventral stream focused on input regions perceived by the fovea part of an eye (foveation) and a dorsal stream providing visual guidance. Iterative processing calibrates visual focus and processes image patches sequentially. The framework trains a label-based DNN for the ventral stream model and reinforcement learning for the dorsal stream model, demonstrating applicability to weakly-supervised object localization (WSOL). The framework can predict an object’s properties and localize it by predicting its bounding box. Additionally, the dorsal model can be applied independently on unseen images from different datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning framework inspired by the human brain helps computers better understand what they see and where things are located. This framework is special because it uses two separate ways of processing visual information, just like our brains do. One way focuses on recognizing objects (what) while the other provides guidance on their location (where). The framework trains these two processes separately and then combines them to improve accuracy. It’s particularly useful for a task called weakly-supervised object localization, where computers have limited training data. The framework can predict an object’s properties and accurately locate it within an image. |
Keywords
» Artificial intelligence » Bounding box » Machine learning » Reinforcement learning » Supervised
