Zhaofeng Niu | Searcharxiv

arXiv2026

Making the Invisible Visible: Toward Micro-Expression Visualization for Empathy in Social Interaction

Micro-expressions are brief and subtle facial movements that convey nuanced affective information but often remain imperceptible during natural social interaction. Although prior research has primarily focused on computational recognition and spotting of micro-expressions, their application in human-centered contexts remains limited. From the perspective of social augmentation, this work proposes a conceptual framework for micro-expression visualization that transforms otherwise imperceptible micro-expressions into perceptible affective cues, with the aim of exploring their potential influence on empathic experience. Furthermore, we outline a planned pilot study to preliminarily assess the feasibility of this framework under controlled conditions.

cs.HC↗

arXiv2023

Concatenated Masked Autoencoders as Spatial-Temporal Learner

Learning representations from videos requires understanding continuous motion and visual correspondences between frames. In this paper, we introduce the Concatenated Masked Autoencoders (CatMAE) as a spatial-temporal learner for self-supervised video representation learning. For the input sequence of video frames, CatMAE keeps the initial frame unchanged while applying substantial masking (95%) to subsequent frames. The encoder in CatMAE is responsible for encoding visible patches for each frame individually; subsequently, for each masked frame, the decoder leverages visible patches from both previous and current frames to reconstruct the original image. Our proposed method enables the model to estimate the motion information between visible patches, match the correspondences between preceding and succeeding frames, and ultimately learn the evolution of scenes. Furthermore, we propose a new data augmentation strategy, Video-Reverse (ViRe), which uses reversed video frames as the model's reconstruction targets. This further encourages the model to utilize continuous motion details and correspondences to complete the reconstruction, thereby enhancing the model's capabilities. Compared to the most advanced pre-training methods, CatMAE achieves a leading level in video segmentation tasks and action recognition tasks.

cs.CV↗