Search Results for author: Apratim Bhattacharyya

Found 19 papers, 5 papers with code

SampleFix: Learning to Correct Programs by Efficient Sampling of Diverse Fixes

no code implementations NeurIPS Workshop CAP 2020 Hossein Hajipour, Apratim Bhattacharyya, Mario Fritz

Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution over potential fixes.

Haar Wavelet based Block Autoregressive Flows for Trajectories

no code implementations21 Sep 2020 Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schiele

This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner.

Normalizing Flows with Multi-Scale Autoregressive Priors

1 code implementation CVPR 2020 Shweta Mahajan, Apratim Bhattacharyya, Mario Fritz, Bernt Schiele, Stefan Roth

Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis.

Density Estimation Image Generation

"Best-of-Many-Samples" Distribution Matching

1 code implementation27 Sep 2019 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.

``"Best-of-Many-Samples" Distribution Matching

no code implementations25 Sep 2019 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction

no code implementations24 Aug 2019 Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz, Bernt Schiele, Christoph-Nikolas Straehle

Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world.

Trajectory Prediction

Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods

1 code implementation ICLR 2019 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence.

Bayesian Inference Precipitation Forecasting

Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective

1 code implementation20 Jun 2018 Apratim Bhattacharyya, Bernt Schiele, Mario Fritz

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.

Human Pose Forecasting

Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization

no code implementations18 Jun 2018 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.

Future prediction Segmentation +1

Accurate and Diverse Sampling of Sequences Based on a “Best of Many” Sample Objective

no code implementations CVPR 2018 Apratim Bhattacharyya, Bernt Schiele, Mario Fritz

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

no code implementations CVPR 2018 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error.

Autonomous Driving Trajectory Prediction

Efficiently Summarising Event Sequences with Rich Interleaving Patterns

no code implementations27 Jan 2017 Apratim Bhattacharyya, Jilles Vreeken

Discovering the key structure of a database is one of the main goals of data mining.

Long-Term Image Boundary Prediction

no code implementations27 Nov 2016 Apratim Bhattacharyya, Mateusz Malinowski, Bernt Schiele, Mario Fritz

Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception.

Spatio-Temporal Image Boundary Extrapolation

no code implementations24 May 2016 Apratim Bhattacharyya, Mateusz Malinowski, Mario Fritz

Furthermore, we show long-term prediction of boundaries in situations where the motion is governed by the laws of physics.

Video Segmentation Video Semantic Segmentation

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