2.4k citations · 12.3k across the 115 of their papers we have counts for
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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
Yan Wang, Wei-Lun Chao, Divyansh Garg +3
3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise bu…
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
Brian H. Wang, Yan Wang, Kilian Q. Weinberger +1
We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appeara…
Anytime Stereo Image Depth Estimation on Mobile Devices
Yan Wang, Zihang Lai, Gao Huang +4
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-o…
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel +2
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficie…
Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
Adversarial images aim to change a target model's decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this…
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan +4
Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address…
Resource Aware Person Re-identification across Multiple Resolutions
Yan Wang, Lequn Wang, Yurong You +6
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details. However…
Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger +1
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent advances in inducing point methods…
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu +2
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kern…