39 citations · 121 across the 16 of their papers we have counts for
18 papers
SciFig: Towards Automating Editable Figure Generation for Scientific Papers
Siyuan Huang, Yifan Zhou, Yutong Gao +8
High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create. Such figures must distill a method's components an…
Instruct2Attack: Language-Guided Semantic Adversarial Attacks
Jiang Liu, Chen Wei, Yuxiang Guo +5
We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions. We make use o…
Whole-body Detection, Recognition and Identification at Altitude and Range
Siyuan Huang, Ram Prabhakar Kathirvel, Chun Pong Lau +1
In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degr…
VILLS -- Video-Image Learning to Learn Semantics for Person Re-Identification
Siyuan Huang, Ram Prabhakar, Yuxiang Guo +2
Person Re-identification is a research area with significant real world applications. Despite recent progress, existing methods face challenges in robust re-identification in the w…
Distillation-guided Representation Learning for Unconstrained Gait Recognition
Yuxiang Guo, Siyuan Huang, Ram Prabhakar +3
Gait recognition holds the promise of robustly identifying subjects based on walking patterns instead of appearance information. While previous approaches have performed well for c…
Attribute-Guided Encryption with Facial Texture Masking
Chun Pong Lau, Jiang Liu, Rama Chellappa
The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on…
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
Jiang Liu, Chun Pong Lau, Zhongliang Guo +3
The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on…
Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
Yuxiang Guo, Cheng Peng, Chun Pong Lau +1
Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait rec…
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Chun Pong Lau, Jiang Liu, Hossein Souri +3
Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not…
Mutual Adversarial Training: Learning together is better than going alone
Jiang Liu, Chun Pong Lau, Hossein Souri +2
Recent studies have shown that robustness to adversarial attacks can be transferred across networks. In other words, we can make a weak model more robust with the help of a strong…
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Jiang Liu, Alexander Levine, Chun Pong Lau +2
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of…
Identification of Attack-Specific Signatures in Adversarial Examples
Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau +2
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks. In many cases, multiple algorithm…
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Wei-An Lin, Chun Pong Lau, Alexander Levine +2
Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the def…
ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence
Chun Pong Lau, Hossein Souri, Rama Chellappa
Image degradation due to atmospheric turbulence is common while capturing images at long ranges. To mitigate the degradation due to turbulence which includes deformation and blur,…
Subsampled Turbulence Removal Network
Wai Ho Chak, Chun Pong Lau, Lok Ming Lui
We present a deep-learning approach to restore a sequence of turbulence-distorted video frames from turbulent deformations and space-time varying blurs. Instead of requiring a mass…
Variational models for joint subsampling and reconstruction of turbulence-degraded images
Chun Pong Lau, Yu Hin Lai, Lok Ming Lui
Turbulence-degraded image frames are distorted by both turbulent deformations and space-time-varying blurs. To suppress these effects, we propose a multi-frame reconstruction schem…
Image retargeting via Beltrami representation
Chun Pong Lau, Chun Pang Yung, Lok Ming Lui
Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of th…
Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps
Chun Pong Lau, Yu Hin Lai, Lok Ming Lui
We address the problem of restoring a high-quality image from an observed image sequence strongly distorted by atmospheric turbulence. A novel algorithm is proposed in this paper t…