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GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation.
Delving Into Egocentric Actions
We propose to utilize these mid-level egocentric cues for egocentric action recognition.
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