122 citations · 572 across the 79 of their papers we have counts for
79 papers
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
Yuemei Ren, Liang Liao, Stephen John Maybank +2
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approac…
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation
Cheng Zhang, Yu Zhu, Qingsen Yan +2
The aim of image restoration is to recover high-quality images from distorted ones. However, current methods usually focus on a single task (\emph{e.g.}, denoising, deblurring or s…
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
Peng Wu, Xuerong Zhou, Guansong Pang +4
The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful vis…
VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation
Congqi Cao, Ze Sun, Qinyi Lv +2
Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Mos…
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
Axi Niu, Pham Xuan Trung, Kang Zhang +4
Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demon…
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
Peng Wu, Jing Liu, Xiangteng He +3
Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame lev…
A Dynamic Feature Interaction Framework for Multi-task Visual Perception
Yuling Xi, Hao Chen, Ning Wang +4
Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve mul…
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-es…
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
Congqi Cao, Yue Lu, Peng Wang +1
Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly…
3D Registration with Maximal Cliques
Xiyu Zhang, Jiaqi Yang, Shikun Zhang +1
As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registratio…
Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Haoyu Wang, Guansong Pang, Peng Wang +3
Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few…
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
Rui Li, Dong Gong, Wei Yin +6
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consis…
A Unified HDR Imaging Method with Pixel and Patch Level
Qingsen Yan, Weiye Chen, Song Zhang +3
Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object mo…
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders
Qingsen Yan, Song Zhang, Weiye Chen +5
Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based method…
Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features
Ke Song, Quan Xia, Guoqiang Liang +2
Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimizatio…
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization
Congqi Cao, Yizhe Wang, Yue Lu +2
Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often negle…
PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image
Zhaoshuai Qi, Xiaojun Liu, Xiaolin Liu +2
As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc. However, capturing mult…
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
Axi Niu, Pei Wang, Yu Zhu +3
Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding performance, they still suffer from hig…
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Qiao Wu, Jiaqi Yang, Kun Sun +3
3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds…
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resol…
Take a Prior from Other Tasks for Severe Blur Removal
Pei Wang, Danna Xue, Yu Zhu +4
Recovering clear structures from severely blurry inputs is a challenging problem due to the large movements between the camera and the scene. Although some works apply segmentation…
New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning
Guoqiang Liang, Zhaojie Chen, Zhaoqiang Chen +2
To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the…
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance
Congqi Cao, Xin Zhang, Shizhou Zhang +2
Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of t…
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
Bingliang Jiao, Lingqiao Liu, Liying Gao +5
In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictabl…