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• W3135844897 abstract "Recognizing the emotional state of a person within the image in real-world scenarios is a key problem in affective computing and has various promising applications. Local regions in the image, including different objects in the background scene and parts within the foreground body, usually have different contributions to emotion perception of the target person. This, however, has not been well exploited in most existing methods. In this article, we propose to make relational region-level analysis to account for the different contributions of different regions to emotion recognition. For the background scene, we propose a Body-Object Attention (BOA) module to estimate the contributions of background objects to emotion recognition given the target foreground body. Within the foreground body, we propose a Body Part Attention (BPA) module to recalibrate the channel-wise body feature responses to attend on body parts that are more important. Moreover, we propose to model the emotion label dependency in real-world images, considering both the semantic meanings of these labels and their co-occurrence patterns. We evaluate the proposed method on the EMOTIC and CAER-S datasets, and experimental results show the superiority of our method compared with the state-of-the-art algorithms." @default.
• W3135844897 created "2021-03-15" @default.
• W3135844897 creator A5031023511 @default.
• W3135844897 creator A5050613147 @default.
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• W3135844897 date "2023-01-01" @default.
• W3135844897 modified "2023-10-18" @default.
• W3135844897 title "Human Emotion Recognition With Relational Region-Level Analysis" @default.
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• W3135844897 doi "https://doi.org/10.1109/taffc.2021.3064918" @default.
• W3135844897 hasPublicationYear "2023" @default.
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