334 citations · 820 across the 10 of their papers we have counts for
10 papers
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang +4
Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accu…
Feature Space Augmentation for Long-Tailed Data
Peng Chu, Xiao Bian, Shaopeng Liu +1
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented c…
Detection and Tracking Meet Drones Challenge
Pengfei Zhu, Longyin Wen, Dawei Du +4
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently,…
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Longyin Wen, Dawei Du, Shengkun Li +2
The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them l…
Evolvement Constrained Adversarial Learning for Video Style Transfer
Wenbo Li, Longyin Wen, Xiao Bian +1
Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games. Many existing methods use optical flow t…
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches
Yuezun Li, Xiao Bian, Ming-ching Chang +1
Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on explor…
Robust Adversarial Perturbation on Deep Proposal-based Models
Yuezun Li, Daniel Tian, Ming-Ching Chang +2
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) met…
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Shifeng Zhang, Longyin Wen, Xiao Bian +2
Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other. In this paper, we propose a new occlusion-aware…
Vision Meets Drones: A Challenge
Pengfei Zhu, Longyin Wen, Xiao Bian +2
In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform. The…
Single-Shot Refinement Neural Network for Object Detection
Shifeng Zhang, Longyin Wen, Xiao Bian +2
For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high effi…