Search Results for author: Carl Doersch

Found 22 papers, 10 papers with code

Input-level Inductive Biases for 3D Reconstruction

no code implementations6 Dec 2021 Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, Andrew Zisserman

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases.

3D Reconstruction Depth Estimation

CrossTransformers: spatially-aware few-shot transfer

2 code implementations NeurIPS 2020 Carl Doersch, Ankush Gupta, Andrew Zisserman

In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains.

Self-Supervised Learning

Sim2real transfer learning for 3D human pose estimation: motion to the rescue

no code implementations NeurIPS 2019 Carl Doersch, Andrew Zisserman

In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person's motion, notably as optical flow and the motion of 2D keypoints.

3D Human Pose Estimation 3D Pose Estimation +2

Structured agents for physical construction

no code implementations5 Apr 2019 Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick

Our results show that agents which use structured representations (e. g., objects and scene graphs) and structured policies (e. g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes.

Scene Understanding

The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR

no code implementations11 Sep 2018 Mateusz Malinowski, Carl Doersch

Visual QA is a pivotal challenge for higher-level reasoning, requiring understanding language, vision, and relationships between many objects in a scene.

Question Answering Relational Reasoning

Learning Visual Question Answering by Bootstrapping Hard Attention

no code implementations ECCV 2018 Mateusz Malinowski, Carl Doersch, Adam Santoro, Peter Battaglia

Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs.

Hard Attention Question Answering +1

Kickstarting Deep Reinforcement Learning

no code implementations10 Mar 2018 Simon Schmitt, Jonathan J. Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami

Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance.

reinforcement-learning

Multi-task Self-Supervised Visual Learning

no code implementations ICCV 2017 Carl Doersch, Andrew Zisserman

We investigate methods for combining multiple self-supervised tasks--i. e., supervised tasks where data can be collected without manual labeling--in order to train a single visual representation.

Depth Estimation General Classification

An Uncertain Future: Forecasting from Static Images using Variational Autoencoders

no code implementations25 Jun 2016 Jacob Walker, Carl Doersch, Abhinav Gupta, Martial Hebert

We show that our method is able to successfully predict events in a wide variety of scenes and can produce multiple different predictions when the future is ambiguous.

Tutorial on Variational Autoencoders

26 code implementations19 Jun 2016 Carl Doersch

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions.

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

Unsupervised Visual Representation Learning by Context Prediction

2 code implementations ICCV 2015 Carl Doersch, Abhinav Gupta, Alexei A. Efros

This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation.

Representation Learning

Mid-level Elements for Object Detection

no code implementations27 Apr 2015 Aayush Bansal, Abhinav Shrivastava, Carl Doersch, Abhinav Gupta

Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data).

Object Detection

Mid-level Visual Element Discovery as Discriminative Mode Seeking

no code implementations NeurIPS 2013 Carl Doersch, Abhinav Gupta, Alexei A. Efros

We also propose the Purity-Coverage plot as a principled way of experimentally analyzing and evaluating different visual discovery approaches, and compare our method against prior work on the Paris Street View dataset.

Scene Classification

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