SocialLDG models six socio-cognitive tasks with lexical priors from language models and time-evolving task affinities via dynamic graphs, claiming state-of-the-art results on two public human-robot interaction datasets plus scalability without forgetting.
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FGINet uses a band-masked frequency encoder and layer-wise gated injection to fuse frequency artifacts with vision foundation model semantics, plus hyperspherical compactness learning, to achieve better generalization in AI-generated image detection.
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Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning
SocialLDG models six socio-cognitive tasks with lexical priors from language models and time-evolving task affinities via dynamic graphs, claiming state-of-the-art results on two public human-robot interaction datasets plus scalability without forgetting.
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Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection
FGINet uses a band-masked frequency encoder and layer-wise gated injection to fuse frequency artifacts with vision foundation model semantics, plus hyperspherical compactness learning, to achieve better generalization in AI-generated image detection.