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This AI Paper from NTU and Apple Unveils OGEN: A Novel AI Approach for Boosting Out-of-Domain Generalization in Vision-Language Models

Large-scale pre-trained vision-language models, exemplified by CLIP (Radford et al., 2021), exhibit remarkable generalizability across diverse visual domains and real-world tasks. However, their zero-shot in-distribution (ID) performance faces limitations on certain downstream datasets. Additionally, when evaluated in a closed-set manner, these models often struggle with out-of-distribution (OOD) samples from novel classes, posing safety risks in…

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Google Deepmind and University of Toronto Researchers’ Breakthrough in Human-Robot Interaction: Utilizing Large Language Models for Generative Expressive Robot Behaviors

Numerous challenges underlying human-robot interaction exist. One such challenge is enabling robots to display human-like expressive behaviors. Traditional rule-based methods need more scalability in new social contexts, while the need for extensive, specific datasets limits data-driven approaches. This limitation becomes pronounced as the variety of social interactions a robot might encounter increases, creating a demand…

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