Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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,Emotion Recognition for?Multiple Context Awareness,certainty in expressing emotions and fail to model multiple context representations complementarily. To alleviate these issues, we present a context-aware emotion recognition framework that combines four complementary contexts. The first context is multimodal emotion recognition based on facial expr

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,Localizing Visual Sounds the?Easy Way,ining. Previous works often seek high audio-visual similarities for likely positive (sounding) regions and low similarities for likely negative regions. However, accurately distinguishing between sounding and non-sounding regions is challenging without manual annotations. In this work, we propose a

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,Learning Visual Styles from?Audio-Visual Associations,nt a method for learning visual styles from unlabeled audio-visual data. Our model learns to manipulate the texture of a scene to match a sound, a problem we term .. Given a dataset of paired audio-visual data, we learn to modify input images such that, after manipulation, they are more likely to co

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,Remote Respiration Monitoring of?Moving Person Using Radio Signals,rious remote applications (e.g., telehealth or emergency detection). The existing nRRM approaches mainly analyze fine details from videos to extract minute respiration signals; however, they have practical limitations in that the head or body of a subject must be quasi-stationary. In this study, we

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,: A Dataset for?Physical Audiovisual CommonSense Reasoning,the physical world. Fundamental to this reasoning is .: understanding the physical properties and affordances of available objects, how they can be manipulated, and how they interact with other objects. Physical commonsense reasoning is fundamentally a multi-sensory task, since physical properties a

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