44 citations · 88 across the 10 of their papers we have counts for
11 papers
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Bingbing Rao, Ehsan Kazemi, Yifan Ding +3
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial me…
Improving Object Detection with Selective Self-supervised Self-training
Yandong Li, Di Huang, Danfeng Qin +2
We study how to leverage Web images to augment human-curated object detection datasets. Our approach is two-pronged. On the one hand, we retrieve Web images by image-to-image searc…
Trace-Norm Adversarial Examples
Ehsan Kazemi, Thomas Kerdreux, Liqiang Wang
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a neighborhood of the original image, the so…
BachGAN: High-Resolution Image Synthesis from Salient Object Layout
Yandong Li, Yu Cheng, Zhe Gan +3
We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout. This new setting allows users to provide th…
Self-supervised learning for audio-visual speaker diarization
Yifan Ding, Yong Xu, Shi-Xiong Zhang +2
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer inte…
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Yunhui Guo, Yandong Li, Liqiang Wang +1
There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which a…
Defending Against Adversarial Attacks Using Random Forests
Yifan Ding, Liqiang Wang, Huan Zhang +3
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortun…
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin, Zhezhi He, Li Yang +3
Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the counte…
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Yifan Ding, Chuan Wang, Haibin Huang +3
In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new chal…
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang +2
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we…
Multi-Stream Dynamic Video Summarization
Mohamed Elfeki, Liqiang Wang, Ali Borji
With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual conte…