no code implementations • 21 Mar 2024 • Minchul Kim, Yiyang Su, Feng Liu, Anil Jain, Xiaoming Liu
By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations.
no code implementations • ICCV 2023 • Feng Liu, Minchul Kim, ZiAng Gu, Anil Jain, Xiaoming Liu
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics.
no code implementations • 29 Jun 2023 • Feng Liu, Ryan Ashbaugh, Nicholas Chimitt, Najmul Hassan, Ali Hassani, Ajay Jaiswal, Minchul Kim, Zhiyuan Mao, Christopher Perry, Zhiyuan Ren, Yiyang Su, Pegah Varghaei, Kai Wang, Xingguang Zhang, Stanley Chan, Arun Ross, Humphrey Shi, Zhangyang Wang, Anil Jain, Xiaoming Liu
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
1 code implementation • CVPR 2023 • Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu
Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.
1 code implementation • 19 Oct 2022 • Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu
Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.
Ranked #1 on Face Verification on IJB-B (TAR @ FAR=0.001 metric)
3 code implementations • 23 Aug 2022 • Xiao Guo, Yaojie Liu, Anil Jain, Xiaoming Liu
In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
2 code implementations • 20 Jul 2022 • Feng Liu, Minchul Kim, Anil Jain, Xiaoming Liu
To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.
Ranked #1 on Face Verification on IJB-S
no code implementations • 9 Jun 2021 • Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
2 code implementations • CVPR 2020 • Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, Anil Jain
Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task.
1 code implementation • 24 Apr 2018 • Debayan Deb, Susan Wiper, Alexandra Russo, Sixue Gong, Yichun Shi, Cori Tymoszek, Anil Jain
We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees.
no code implementations • 6 Dec 2015 • Qi Qian, Inci M. Baytas, Rong Jin, Anil Jain, Shenghuo Zhu
The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.