most citedMB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing

10 citations · 19 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2024

Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News

Qixuan Zhang, Zhifeng Wang, Yang Liu +4

In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resou…

cs.CV2024

Homography Guided Temporal Fusion for Road Line and Marking Segmentation

Shan Wang, Chuong Nguyen, Jiawei Liu +6

Reliable segmentation of road lines and markings is critical to autonomous driving. Our work is motivated by the observations that road lines and markings are (1) frequently occlud…

cs.CV2024

LRDif: Diffusion Models for Under-Display Camera Emotion Recognition

Zhifeng Wang, Kaihao Zhang, Ramesh Sankaranarayana

This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC). To a…

cs.CV20241 cited

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

Zhe Kong, Wentian Zhang, Tao Wang +4

Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Althoug…

cs.CV2023

Deep Video Restoration for Under-Display Camera

Xuanxi Chen, Tao Wang, Ziqian Shao +6

Images or videos captured by the Under-Display Camera (UDC) suffer from severe degradation, such as saturation degeneration and color shift. While restoration for UDC has been a cr…

cs.CV202310 cited

MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing

Yuwei Qiu, Kaihao Zhang, Chenxi Wang +3

In recent years, Transformer networks are beginning to replace pure convolutional neural networks (CNNs) in the field of computer vision due to their global receptive field and ada…