activity
20202024
most citedMVT: Mask Vision Transformer for Facial Expression Recognition in the wild

48 citations · 55 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024★ 1 cited

From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos

Hanting Li, Hongjing Niu, Feng Zhao

Micro-expression recognition (MER) has drawn increasing attention in recent years due to its potential applications in intelligent medical and lie detection. However, the shortage…

cs.CV2022★ 2 cited

AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

Zehui Chen, Zhenyu Li, Shiquan Zhang +5

Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sou…

cs.CV2022

MMNet: Muscle motion-guided network for micro-expression recognition

Hanting Li, Mingzhe Sui, Zhaoqing Zhu +1

Facial micro-expressions (MEs) are involuntary facial motions revealing peoples real feelings and play an important role in the early intervention of mental illness, the national s…

cs.CV2021

Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

Zizheng Yang, Xin Jin, Kecheng Zheng +1

Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID i…

cs.CV2021★ 4 cited

MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition

Hanting Li, Mingzhe Sui, Zhaoqing Zhu +1

Vision transformer (ViT) has been widely applied in many areas due to its self-attention mechanism that help obtain the global receptive field since the first layer. It even achiev…

cs.CV2021★ 48 cited

MVT: Mask Vision Transformer for Facial Expression Recognition in the wild

Hanting Li, Mingzhe Sui, Feng Zhao +2

Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of…