no code implementations • 14 Mar 2024 • Soroush Seifi, Daniel Olmeda Reino, Fabien Despinoy, Rahaf Aljundi
Semantic Segmentation is one of the most challenging vision tasks, usually requiring large amounts of training data with expensive pixel level annotations.
no code implementations • 3 Oct 2023 • Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin, Rahaf Aljundi
Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning.
1 code implementation • ICCV 2021 • Soroush Seifi, Abhishek Jha, Tinne Tuytelaars
In this paper, we propose the Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the visual exploration instead of task-specific uncertainty maps; (b) can be used for both dense and sparse prediction tasks; and (c) uses a contrastive stream to further improve the representations learned.
no code implementations • ECCV 2020 • Soroush Seifi, Tinne Tuytelaars
The main idea is to refine an agent's understanding of the environment by attending the areas it is most uncertain about.
no code implementations • 23 Sep 2019 • Soroush Seifi, Tinne Tuytelaars
Convolutional neural networks (CNNs) and transfer learning have recently been used for 6 degrees of freedom (6-DoF) camera pose estimation.
no code implementations • 23 Sep 2019 • Soroush Seifi, Tinne Tuytelaars
We address the problem of active visual exploration of large 360{\deg} inputs.