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01/23/2023
Long-tail Detection with Effective Class-Margins
Large-scale object detection and instance segmentation face a severe dat...
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12/12/2022
NMS Strikes Back
Detection Transformer (DETR) directly transforms queries to unique objec...
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05/05/2022
Cross-view Transformers for real-time Map-view Semantic Segmentation
We present cross-view transformers, an efficient attention-based model f...
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03/22/2022
Learning from All Vehicles
In this paper, we present a system to train driving policies from experi...
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11/12/2021
Multimodal Virtual Point 3D Detection
Lidar-based sensing drives current autonomous vehicles. Despite rapid pr...
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06/21/2021
Towards Long-Form Video Understanding
Our world offers a never-ending stream of visual stimuli, yet today's vi...
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05/03/2021
Learning to drive from a world on rails
We learn an interactive vision-based driving policy from pre-recorded dr...
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03/12/2021
Probabilistic two-stage detection
We develop a probabilistic interpretation of two-stage object detection....
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02/25/2021
Simple multi-dataset detection
How do we build a general and broad object detection system? We use all ...
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10/27/2020
Memory Optimization for Deep Networks
Deep learning is slowly, but steadily, hitting a memory bottleneck. Whil...
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08/27/2020
Domain Adaptation Through Task Distillation
Deep networks devour millions of precisely annotated images to build the...
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06/19/2020
Center-based 3D Object Detection and Tracking
Three-dimensional objects are commonly represented as 3D boxes in a poin...
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04/06/2020
Lossless Image Compression through Super-Resolution
We introduce a simple and efficient lossless image compression algorithm...
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04/02/2020
Tracking Objects as Points
Tracking has traditionally been the art of following interest points thr...
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12/27/2019
Learning by Cheating
Vision-based urban driving is hard. The autonomous system needs to learn...
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12/02/2019
A Multigrid Method for Efficiently Training Video Models
Training competitive deep video models is an order of magnitude slower t...
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05/30/2019
Does computer vision matter for action?
Computer vision produces representations of scene content. Much computer...
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05/16/2019
Monocular Plan View Networks for Autonomous Driving
Convolutions on monocular dash cam videos capture spatial invariances in...
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04/16/2019
Objects as Points
Detection identifies objects as axis-aligned boxes in an image. Most suc...
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01/23/2019
Bottom-up Object Detection by Grouping Extreme and Center Points
With the advent of deep learning, object detection drifted from a bottom...
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12/12/2018
Long-Term Feature Banks for Detailed Video Understanding
To understand the world, we humans constantly need to relate the present...
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11/26/2018
Joint Monocular 3D Vehicle Detection and Tracking
3D vehicle detection and tracking from a monocular camera requires detec...
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10/29/2018
Assessing Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) has achieved breakthrough results on ma...
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04/18/2018
Video Compression through Image Interpolation
An ever increasing amount of our digital communication, media consumptio...
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12/02/2017
Compressed Video Action Recognition
Training robust deep video representations has proven to be much more ch...
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06/23/2017
Sampling Matters in Deep Embedding Learning
Deep embeddings answer one simple question: How similar are two images? ...
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09/12/2016
Generative Visual Manipulation on the Natural Image Manifold
Realistic image manipulation is challenging because it requires modifyin...
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05/31/2016
Adversarial Feature Learning
The ability of the Generative Adversarial Networks (GANs) framework to l...
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04/25/2016
Context Encoders: Feature Learning by Inpainting
We present an unsupervised visual feature learning algorithm driven by c...
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04/18/2016
Learning Dense Correspondence via 3D-guided Cycle Consistency
Discriminative deep learning approaches have shown impressive results fo...
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10/08/2015
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
We propose a data-driven approach for intrinsic image decomposition, whi...
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06/11/2015
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
We present an approach to learn a dense pixel-wise labeling from image-l...
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10/20/2012
