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Hannes Schulz

6 accepted papers

2021

Decomposed Mutual Information Estimation for Contrastive Representation Learning

Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoff Gordon, Philip Bachman, Remi Tachet Des Combes
ICML 2021spotlight

Recent contrastive representation learning methods rely on estimating mutual information (MI) between multiple views of an underlying context. E.g., we can derive multiple views of a given image by applying data augmentation, or we can split a sequence into views comprising the past and future of so…

Cited by 43Source ↗PDF ↗Scholar ↗
2020

Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer

Igor Shalyminov, Alessandro Sordoni, Adam Atkinson, Hannes Schulz
ICASSP 2020accepted

Goal-oriented dialogue systems are now widely adopted in industry, where practical aspects of using them becomes of key importance. As such, it is expected from such systems to fit into a rapid prototyping cycle for new products and domains. For data-driven dialogue systems (especially those based o…

Cited by 0Source ↗Scholar ↗
2019

Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction

Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou +2
ICCV 2019poster

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates…

Cited by 91PDF ↗Scholar ↗
2018

Towards Deep Conversational Recommendations

Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
NeurIPS 2018poster

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that of…

Cited by 484Source ↗PDF ↗Scholar ↗
2017

Combining Semantic and Geometric Features for Object Class Segmentation of Indoor Scenes

Farzad Husain, Hannes Schulz, Babette Dellen, Carme Torras, Sven Behnke
RA-L 2017

Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously used in indoor robotics. We investigate strategies for efficient pixelwise

Cited by 54Source ↗Scholar ↗
2015

RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features

Max Schwarz, Hannes Schulz, Sven Behnke
ICRA 2015poster

Object recognition and pose estimation from RGB-D images are important tasks for manipulation robots which can be learned from examples. Creating and annotating datasets for learning is expensive, however. We address this problem with transfer learning from deep convolutional neural networks (CNN) t…

Cited by 437Source ↗Scholar ↗