551 citations · 2.3k across the 76 of their papers we have counts for
12 papers · 1 filter
Look, Listen, and Act: Towards Audio-Visual Embodied Navigation
Chuang Gan, Yiwei Zhang, Jiajun Wu +2
A crucial ability of mobile intelligent agents is to integrate the evidence from multiple sensory inputs in an environment and to make a sequence of actions to reach their goals. I…
Adversarial Examples Improve Image Recognition
Cihang Xie, Mingxing Tan, Boqing Gong +3
Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if h…
Open Compound Domain Adaptation
Ziwei Liu, Zhongqi Miao, Xingang Pan +4
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a…
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao +3
We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is tr…
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan +1
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic…
A Fast and Accurate One-Stage Approach to Visual Grounding
Zhengyuan Yang, Boqing Gong, Liwei Wang +3
We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight. The performances of existing propose-and-rank two-stage methods 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…
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…
Large-Scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known…
Synthesized Policies for Transfer and Adaptation across Tasks and Environments
Hexiang Hu, Liyu Chen, Boqing Gong +1
The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simulta…
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
Xianfeng Tang, Boqing Gong, Yanwei Yu +4
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations w…
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
Yang Zhang, Philip David, Hassan Foroosh +1
During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous…