220 citations · 465 across the 23 of their papers we have counts for
13 papers · 1 filter
Towards Building More Robust Models with Frequency Bias
Qingwen Bu, Dong Huang, Heming Cui
The vulnerability of deep neural networks to adversarial samples has been a major impediment to their broad applications, despite their success in various fields. Recently, some wo…
Zero-shot Model Diagnosis
Jinqi Luo, Zhaoning Wang, Chen Henry Wu +2
When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluat…
DensePose From WiFi
Jiaqi Geng, Dong Huang, Fernando De la Torre
Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, hum…
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Zongyuan Zhang, Qingwen Bu, Tianyang Duan +5
Deep neural networks (DNNs) are vulnerable to adversarial examples, in which DNNs are misled to false outputs due to inputs containing imperceptible perturbations. Adversarial trai…
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation
Zechun Liu, Kwang-Ting Cheng, Dong Huang +2
The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior represent…
Elaborative Rehearsal for Zero-shot Action Recognition
Shizhe Chen, Dong Huang
The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to recog…
Self-Challenging Improves Cross-Domain Generalization
Zeyi Huang, Haohan Wang, Eric P. Xing +1
Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. When the training and testing data are under similar d…
Multiple Anchor Learning for Visual Object Detection
Wei Ke, Tianliang Zhang, Zeyi Huang +3
Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate…
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking
Jimuyang Zhang, Sanping Zhou, Jinjun Wang +1
The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames. Standard motion estimators used in tracking…
Can WiFi Estimate Person Pose?
Fei Wang, Stanislav Panev, Ziyi Dai +2
WiFi human sensing has achieved great progress in indoor localization, activity classification, etc. Retracing the development of these work, we have a natural question: can WiFi d…
SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
Sanping Zhou, Jimuyang Zhang, Jinjun Wang +2
Deep convolutional neural network significantly boosted the capability of salient object detection in handling large variations of scenes and object appearances. However, convoluti…
Person-in-WiFi: Fine-grained Person Perception using WiFi
Fei Wang, Sanping Zhou, Stanislav Panev +2
Fine-grained person perception such as body segmentation and pose estimation has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars (e.g., RF-Pose) and LiD…
Improving Object Detection with Inverted Attention
Zeyi Huang, Wei Ke, Dong Huang
Improving object detectors against occlusion, blur and noise is a critical step to deploy detectors in real applications. Since it is not possible to exhaust all image defects thro…