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20232025
most citedToward Better SSIM Loss for Unsupervised Monocular Depth Estimation

5 citations · 6 across the 5 of their papers we have counts for

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cs.CV2025

A biological vision inspired framework for machine perception of abutting grating illusory contours

Xiao Zhang, Kai-Fu Yang, Xian-Shi Zhang +3

Higher levels of machine intelligence demand alignment with human perception and cognition. Deep neural networks (DNN) dominated machine intelligence have demonstrated exceptional…

cs.CV2025★ 5 cited

Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation

Yijun Cao, Fuya Luo, Yongjie Li

Unsupervised monocular depth learning generally relies on the photometric relation among temporally adjacent images. Most of previous works use both mean absolute error (MAE) and s…

cs.CV2024

PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encoding

Wang-Wang Yu, Kai-Fu Yang, Xiangrui Hu +3

The task of macro- and micro-expression spotting aims to precisely localize and categorize temporal expression instances within untrimmed videos. Given the sparse distribution and…

cs.CV2024★ 1 cited

Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting

Wang-Wang Yu, Xian-Shi Zhang, Fu-Ya Luo +4

Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient infor…

cs.CV2023

Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency

Wang-Wang Yu, Kai-Fu Yang, Hong-Mei Yan +1

Most micro- and macro-expression spotting methods in untrimmed videos suffer from the burden of video-wise collection and frame-wise annotation. Weakly-supervised expression spotti…