Search Results for author: Adam R. Kosiorek

Found 17 papers, 12 papers with code

Adversarial Masking for Self-Supervised Learning

1 code implementation31 Jan 2022 Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek

We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective.

Representation Learning Self-Supervised Learning +1

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation

1 code implementation2 Apr 2021 Karl Stelzner, Kristian Kersting, Adam R. Kosiorek

We present ObSuRF, a method which turns a single image of a scene into a 3D model represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to a different object.

Image Segmentation Object +1

NeRF-VAE: A Geometry Aware 3D Scene Generative Model

1 code implementation1 Apr 2021 Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Soňa Mokrá, Danilo J. Rezende

We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering.

Improving End-to-End Object Tracking Using Relational Reasoning

no code implementations ICLR 2020 Fabian B. Fuchs, Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, Ingmar Posner

Relational reasoning, the ability to model interactions and relations between objects, is valuable for robust multi-object tracking and pivotal for trajectory prediction.

Multi-Object Tracking Object +2

MetaFun: Meta-Learning with Iterative Functional Updates

1 code implementation ICML 2020 Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one.

Few-Shot Image Classification Meta-Learning

Stacked Capsule Autoencoders

12 code implementations NeurIPS 2019 Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton

In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.

Cross-Modal Retrieval Object +1

Revisiting Reweighted Wake-Sleep

no code implementations ICLR 2019 Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

9 code implementations1 Oct 2018 Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.

3D Shape Recognition Few-Shot Image Classification +1

Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes

no code implementations14 Jun 2018 Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner

Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset.

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

1 code implementation NeurIPS 2018 Adam R. Kosiorek, Hyunjik Kim, Ingmar Posner, Yee Whye Teh

It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects.

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

1 code implementation ICLR 2019 Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables.

Tighter Variational Bounds are Not Necessarily Better

3 code implementations ICML 2018 Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator.

Hierarchical Attentive Recurrent Tracking

1 code implementation NeurIPS 2017 Adam R. Kosiorek, Alex Bewley, Ingmar Posner

Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori.

Activity Recognition Object +1

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