no code implementations • 27 Sep 2023 • Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yi Wen, Yiwen Song, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, Devi Parikh
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
1 code implementation • CVPR 2023 • Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, Dhruv Mahajan
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems.
1 code implementation • 13 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)
no code implementations • 28 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.
1 code implementation • 24 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.
2 code implementations • NeurIPS 2021 • Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Luc van Gool
Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection.
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.
Ranked #3 on Unsupervised Semantic Segmentation on ImageNet-S-50
no code implementations • 18 Sep 2020 • Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic, Yu Liu, Luc van Gool, Matthew Blaschko, Tinne Tuytelaars, Marie-Francine Moens
In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario.
Ranked #3 on Referring Expression Comprehension on Talk2Car
2 code implementations • ECCV 2020 • Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc van Gool
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
Ranked #4 on Unsupervised Image Classification on ImageNet
1 code implementation • 28 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.
1 code implementation • 20 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.
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.
Ranked #7 on Semantic Segmentation on UrbanLF
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.
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.
no code implementations • 8 Mar 2019 • Simon Vandenhende, Bert de Brabandere, Davy Neven, Luc van Gool
The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier.