21 citations · 44 across the 11 of their papers we have counts for
8 papers · 1 filter
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image
Jianhui Li, Jianmin Li, Haoji Zhang +5
We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map…
Learning Road Scene-level Representations via Semantic Region Prediction
Zihao Xiao, Alan Yuille, Yi-Ting Chen
In this work, we tackle two vital tasks in automated driving systems, i.e., driver intent prediction and risk object identification from egocentric images. Mainly, we investigate t…
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
Xiao Yang, Yinpeng Dong, Tianyu Pang +3
Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition s…
Nuisance-Label Supervision: Robustness Improvement by Free Labels
Xinyue Wei, Weichao Qiu, Yi Zhang +2
In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a tas…
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models
Zihao Xiao, Xianfeng Gao, Chilin Fu +5
Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security se…
Benchmarking Adversarial Robustness
Yinpeng Dong, Qi-An Fu, Xiao Yang +4
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts h…
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition
Yi Zhang, Xinyue Wei, Weichao Qiu +3
Despite the rapid growth in datasets for video activity, stable robust activity recognition with neural networks remains challenging. This is in large part due to the explosion of…
DASZL: Dynamic Action Signatures for Zero-shot Learning
Tae Soo Kim, Jonathan D. Jones, Michael Peven +6
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training o…