358 citations · 1.1k across the 59 of their papers we have counts for
17 papers · 1 filter
Generalizing Event-Based Motion Deblurring in Real-World Scenarios
Xiang Zhang, Lei Yu, Wen Yang +2
Event-based motion deblurring has shown promising results by exploiting low-latency events. However, current approaches are limited in their practical usage, as they assume the sam…
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
Changcheng Xiao, Qiong Cao, Yujie Zhong +4
Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accura…
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events
Yangguang Wang, Xiang Zhang, Mingyuan Lin +4
Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to t…
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
Yuwei Yin, Jean Kaddour, Xiang Zhang +4
Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise inje…
Recovering Continuous Scene Dynamics from A Single Blurry Image with Events
Zhangyi Cheng, Xiang Zhang, Lei Yu +3
This paper aims at demystifying a single motion-blurred image with events and revealing temporally continuous scene dynamics encrypted behind motion blurs. To achieve this end, an…
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
Xiang Zhang, Taoyue Wang, Xiaotian Li +2
Contrastive learning has shown promising potential for learning robust representations by utilizing unlabeled data. However, constructing effective positive-negative pairs for cont…
Learning to Super-Resolve Blurry Images with Events
Lei Yu, Bishan Wang, Xiang Zhang +4
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we…
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4
In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using label…
Learning to See Through with Events
Lei Yu, Xiang Zhang, Wei Liao +2
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi…
FIXED: Frustratingly Easy Domain Generalization with Mixup
Wang Lu, Jindong Wang, Han Yu +4
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augme…
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Xiang Zhang, Huiyuan Yang, Taoyue Wang +2
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection. However, the heterogeneity of multi-modal data poses chall…
Unifying Motion Deblurring and Frame Interpolation with Events
Xiang Zhang, Lei Yu
Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. T…
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Wenju Zhang, Xiang Zhang, Qing Liao +5
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new…
Learning to Purification for Unsupervised Person Re-identification
Long Lan, Xiao Teng, Jing Zhang +2
Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by…
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels
Xiaotian Li, Xiang Zhang, Taoyue Wang +1
Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations. However, manually AU labeling is difficult, time-consuming,…
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers
Xiang Zhang, Lijun Yin
Multi-modal learning has been intensified in recent years, especially for applications in facial analysis and action unit detection whilst there still exist two main challenges in…
BM-NAS: Bilevel Multimodal Neural Architecture Search
Yihang Yin, Siyu Huang, Xiang Zhang
Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems. However, it often requires huge efforts to adapt DNNs to individual multimodal…