no code implementations • 1 May 2024 • Gustavo Adolfo Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah, Ismail Ben Ayed, Christian Desrosiers
In this study, we introduce CLIP Adaptation duRing Test-Time (CLIPArTT), a fully test-time adaptation (TTA) approach for CLIP, which involves automatic text prompts construction during inference for their use as text supervision.
1 code implementation • 12 Apr 2024 • David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah, Ismail Ben Ayed, Christian Desrosiers
Despite their exceptional performance in vision tasks, deep learning models often struggle when faced with domain shifts during testing.
1 code implementation • ICCV 2023 • Gustavo A. Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ismail Ben Ayed, Christian Desrosiers
Deep Learning models have shown remarkable performance in a broad range of vision tasks.
1 code implementation • 28 Mar 2023 • Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Gustavo A. Vargas Hakim, David Osowiechi, Ismail Ben Ayed, Christian Desrosiers
This paper presents a first Token-level Feature Stylization (TFS-ViT) approach for domain generalization, which improves the performance of ViTs to unseen data by synthesizing new domains.
1 code implementation • 20 Oct 2022 • David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ismail Ben Ayed, Christian Desrosiers
A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time.
no code implementations • 4 Apr 2020 • Mehrdad Noori, Ali Bahri, Karim Mohammadi
Most of the existing methods, especially UNet-based networks, integrate low-level and high-level features in a naive way, which may result in confusion for the model.
1 code implementation • 3 Apr 2020 • Mehrdad Noori, Sina Mohammadi, Sina Ghofrani Majelan, Ali Bahri, Mohammad Havaei
To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps.
3 code implementations • 29 Nov 2019 • Sina Mohammadi, Mehrdad Noori, Ali Bahri, Sina Ghofrani Majelan, Mohammad Havaei
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results.