Search Results for author: Simon Vandenhende

Found 15 papers, 10 papers with code

Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

1 code implementation13 Jun 2022 Wouter Van Gansbeke, Simon Vandenhende, Luc van Gool

This paper presents MaskDistill: a novel framework for unsupervised semantic segmentation based on three key ideas.

Ranked #4 on Unsupervised Semantic Segmentation on PASCAL VOC 2012 val (using extra training data)

Object Segmentation +1

Multi-Task Learning for Visual Scene Understanding

no code implementations28 Mar 2022 Simon Vandenhende

Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task.

Multi-Task Learning Scene Understanding

Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals

1 code implementation24 Mar 2022 Simon Vandenhende, Dhruv Mahajan, Filip Radenovic, Deepti Ghadiyaram

A visual counterfactual explanation replaces image regions in a query image with regions from a distractor image such that the system's decision on the transformed image changes to the distractor class.

counterfactual Counterfactual Explanation +1

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

2 code implementations ICCV 2021 Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Luc van Gool

To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings.

Clustering Object +2

Multi-Task Learning for Dense Prediction Tasks: A Survey

1 code implementation28 Apr 2020 Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, Luc van Gool

In this survey, we provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision, explicitly emphasizing on dense prediction tasks.

Multi-Task Learning

A Baseline for the Commands For Autonomous Vehicles Challenge

1 code implementation20 Apr 2020 Simon Vandenhende, Thierry Deruyttere, Dusan Grujicic

The Commands For Autonomous Vehicles (C4AV) challenge requires participants to solve an object referral task in a real-world setting.

Autonomous Vehicles Object

MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning

1 code implementation ECCV 2020 Simon Vandenhende, Stamatios Georgoulis, Luc van Gool

In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup.

Multi-Task Learning Semantic Segmentation

Talk2Car: Taking Control of Your Self-Driving Car

1 code implementation IJCNLP 2019 Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic, Luc van Gool, Marie-Francine Moens

Or more specifically, we consider the problem in an autonomous driving setting, where a passenger requests an action that can be associated with an object found in a street scene.

Autonomous Driving Object +2

Branched Multi-Task Networks: Deciding What Layers To Share

no code implementations ICLR 2020 Simon Vandenhende, Stamatios Georgoulis, Bert de Brabandere, Luc van Gool

In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.

Multi-Task Learning Neural Architecture Search

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