162 citations · 352 across the 4 of their papers we have counts for
7 papers
Global Tracking Transformers
Xingyi Zhou, Tianwei Yin, Vladlen Koltun +1
We present a novel transformer-based architecture for global multi-object tracking. Our network takes a short sequence of frames as input and produces global trajectories for all o…
Probabilistic two-stage detection
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl
We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. I…
Tracking Objects as Points
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl
Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated b…
Objects as Points
Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each.…
Bottom-up Object Detection by Grouping Extreme and Center Points
Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl
With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of objec…
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation
Xingyi Zhou, Arjun Karpur, Linjie Luo +1
Semantic keypoints provide concise abstractions for a variety of visual understanding tasks. Existing methods define semantic keypoints separately for each category with a fixed nu…