3k citations · 5k across the 31 of their papers we have counts for
27 papers · 1 filter
Long-tail Detection with Effective Class-Margins
Jang Hyun Cho, Philipp Krähenbühl
Large-scale object detection and instance segmentation face a severe data imbalance. The finer-grained object classes become, the less frequent they appear in our datasets. However…
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…
Multimodal Virtual Point 3D Detection
Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current Lidar sensors still lag two decades behind traditional color cameras in terms of resolution…
Towards Long-Form Video Understanding
Chao-Yuan Wu, Philipp Krähenbühl
Our world offers a never-ending stream of visual stimuli, yet today's vision systems only accurately recognize patterns within a few seconds. These systems understand the present,…
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…
Simple multi-dataset detection
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl
How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxo…
Center-based 3D Object Detection and Tracking
Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with ad…
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…
Monocular Plan View Networks for Autonomous Driving
Dequan Wang, Coline Devin, Qi-Zhi Cai +2
Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth. We propose a simple transformation…
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
Computer vision produces representations of scene content. Much computer vision research is predicated on the assumption that these intermediate representations are useful for acti…
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…
Long-Term Feature Banks for Detailed Video Understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan +3
To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We p…
Joint Monocular 3D Vehicle Detection and Tracking
Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang +5
Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper,…
Video Compression through Image Interpolation
Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl
An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives throu…
Compressed Video Action Recognition
Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu +3
Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video st…
Sampling Matters in Deep Embedding Learning
Chao-Yuan Wu, R. Manmatha, Alexander J. Smola +1
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most pr…
Generative Visual Manipulation on the Natural Image Manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman +1
Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user…
Learning Dense Correspondence via 3D-guided Cycle Consistency
Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry +2
Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or…
Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue +2
We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional…
Constrained Structured Regression with Convolutional Neural Networks
Deepak Pathak, Philipp Krähenbühl, Stella X. Yu +1
Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision. If provided with enough training data, they predict almost any visual quantity.…
Data-dependent Initializations of Convolutional Neural Networks
Philipp Krähenbühl, Carl Doersch, Jeff Donahue +1
Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models…
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs +4
Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-i…
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
Tinghui Zhou, Philipp Krähenbühl, Alexei A. Efros
We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this…