Search Results for author: Philipp Krähenbühl

Found 32 papers, 26 papers with code

Multimodal Virtual Point 3D Detection

1 code implementation NeurIPS 2021 Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl

For autonomous driving, this means that large objects close to the sensors are easily visible, but far-away or small objects comprise only one measurement or two.

3D Object Detection Autonomous Driving

Towards Long-Form Video Understanding

1 code implementation CVPR 2021 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.

Action Recognition Video Recognition +1

Learning to drive from a world on rails

1 code implementation ICCV 2021 Dian Chen, Vladlen Koltun, Philipp Krähenbühl

This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle.

Autonomous Driving Model-based Reinforcement Learning

Probabilistic two-stage detection

2 code implementations12 Mar 2021 Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl

We develop a probabilistic interpretation of two-stage object detection.

Ranked #16 on Object Detection on COCO test-dev (using extra training data)

Object Detection Region Proposal

Domain Adaptation Through Task Distillation

1 code implementation27 Aug 2020 Brady Zhou, Nimit Kalra, Philipp Krähenbühl

We use these recognition datasets to link up a source and target domain to transfer models between them in a task distillation framework.

Autonomous Driving Domain Adaptation

Tracking Objects as Points

5 code implementations ECCV 2020 Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl

Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.

Multi-Object Tracking Multiple Object Tracking +1

A Multigrid Method for Efficiently Training Video Models

3 code implementations CVPR 2020 Chao-yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krähenbühl

We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU).

Action Detection Action Recognition +1

Does computer vision matter for action?

no code implementations30 May 2019 Brady Zhou, Philipp Krähenbühl, Vladlen Koltun

Thus the central question of our work: Does computer vision matter for action?

Monocular Plan View Networks for Autonomous Driving

no code implementations16 May 2019 Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.

3D Object Detection Autonomous Driving

Don't let your Discriminator be fooled

no code implementations ICLR 2019 Brady Zhou, Philipp Krähenbühl

We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively.

Adversarial Robustness

Objects as Points

71 code implementations16 Apr 2019 Xingyi Zhou, Dequan Wang, Philipp Krähenbühl

We model an object as a single point --- the center point of its bounding box.

Keypoint Detection Real-Time Object Detection

Joint Monocular 3D Vehicle Detection and Tracking

1 code implementation ICCV 2019 Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, Fisher Yu

The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.

3D Object Detection 3D Pose Estimation +4

Video Compression through Image Interpolation

1 code implementation ECCV 2018 Chao-yuan Wu, Nayan Singhal, Philipp Krähenbühl

An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos.

Video Compression

Generative Visual Manipulation on the Natural Image Manifold

1 code implementation12 Sep 2016 Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros

Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result.

Image Manipulation

Adversarial Feature Learning

10 code implementations31 May 2016 Jeff Donahue, Philipp Krähenbühl, Trevor Darrell

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution.

Learning Dense Correspondence via 3D-guided Cycle Consistency

no code implementations CVPR 2016 Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry, Qi-Xing Huang, Alexei A. Efros

We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth.

Data-dependent Initializations of Convolutional Neural Networks

2 code implementations21 Nov 2015 Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell

Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable.

Image Classification Object Detection +1

Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition

no code implementations ICCV 2015 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.

Image Relighting Intrinsic Image Decomposition

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

1 code implementation ICCV 2015 Deepak Pathak, Philipp Krähenbühl, Trevor Darrell

We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i. e. predicted label distribution) of a CNN.

Semantic Segmentation TAG +1

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

3 code implementations20 Oct 2012 Philipp Krähenbühl, Vladlen Koltun

In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image.

Semantic Segmentation

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