86 citations · 296 across the 22 of their papers we have counts for
8 papers · 1 filter
Satellite Image Semantic Segmentation
Eric Guérin, Killian Oechslin, Christian Wolf +1
In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building,…
SIM2REALVIZ: Visualizing the Sim2Real Gap in Robot Ego-Pose Estimation
Theo Jaunet, Guillaume Bono, Romain Vuillemot +1
The Robotics community has started to heavily rely on increasingly realistic 3D simulators for large-scale training of robots on massive amounts of data. But once robots are deploy…
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently re…
Supervising the Transfer of Reasoning Patterns in VQA
Corentin Kervadec, Christian Wolf, Grigory Antipov +2
Methods for Visual Question Anwering (VQA) are notorious for leveraging dataset biases rather than performing reasoning, hindering generalization. It has been recently shown that b…
Universal Domain Adaptation in Ordinal Regression
Boris Chidlovskii, Assem Sadek, Christian Wolf
We address the problem of universal domain adaptation (UDA) in ordinal regression (OR), which attempts to solve classification problems in which labels are not independent, but fol…
How Transferable are Reasoning Patterns in VQA?
Corentin Kervadec, Theo Jaunet, Grigory Antipov +3
Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing hi…
VisQA: X-raying Vision and Language Reasoning in Transformers
Theo Jaunet, Corentin Kervadec, Romain Vuillemot +3
Visual Question Answering systems target answering open-ended textual questions given input images. They are a testbed for learning high-level reasoning with a primary use in HCI,…
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
Brendan Duke, Abdalla Ahmed, Christian Wolf +2
In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable…