32 citations · 55 across the 32 of their papers we have counts for
10 papers · 1 filter
Learning Adaptive Lighting via Channel-Aware Guidance
Qirui Yang, Peng-Tao Jiang, Hao Zhang +4
Learning lighting adaptation is a crucial step in achieving good visual perception and supporting downstream vision tasks. Current research often addresses individual light-related…
Learning Differential Pyramid Representation for Tone Mapping
Qirui Yang, Yinbo Li, Yihao Liu +5
Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textur…
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
Bohao Xing, Zitong Yu, Xin Liu +6
Facial expression recognition (FER) is an important research topic in emotional artificial intelligence. In recent decades, researchers have made remarkable progress. However, curr…
From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation
Xin Liu, Chao Hao, Zitong Yu +2
The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and t…
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter
Chao Liu, Xin Liu, Zitong Yu +3
Deep neural networks (DNNs) have been applied in many computer vision tasks and achieved state-of-the-art (SOTA) performance. However, misclassification will occur when DNNs predic…
Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model
Cong Cao, Huanjing Yue, Xin Liu +1
Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement. However, directly applying the…
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
Chunyang Bi, Xin Luo, Sheng Shen +3
Diffusion models, known for their powerful generative capabilities, play a crucial role in addressing real-world super-resolution challenges. However, these models often focus on i…
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
Kaishen Yuan, Zitong Yu, Xin Liu +3
Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting iss…
A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection
Chao Hao, Zitong Yu, Xin Liu +3
Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though shar…
KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching
Huanjing Yue, Zifan Cui, Kun Li +1
Dual-lens super-resolution (SR) is a practical scenario for reference (Ref) based SR by utilizing the telephoto image (Ref) to assist the super-resolution of the low-resolution wid…