1 code implementation • 9 Nov 2022 • Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova, Wenjie Zi, Hossein Hajimirsadeghi
We introduce some algorithmic improvements to enable training a ViT model from scratch with limited hardware (1 GPU) and time (24 hours) resources.
no code implementations • 25 Feb 2021 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Thibaut Durand, Greg Mori
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.
no code implementations • CVPR 2020 • Thibaut Durand
In this paper, we introduce an open vocabulary model for image hashtag prediction - the task of mapping an image to its accompanying hashtags.
no code implementations • 18 Oct 2019 • Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, JiaWei He, Thibaut Durand, Marcus Brubaker, Greg Mori
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
no code implementations • pproximateinference AABI Symposium 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
no code implementations • pproximateinference AABI Symposium 2019 • Micael Carvalho, Thibaut Durand, JiaWei He, Nazanin Mehrasa, Greg Mori
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.
no code implementations • 25 Sep 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Learning from only partially-observed data for imputation has been an active research area.
2 code implementations • ICCV 2019 • Akash Abdu Jyothi, Thibaut Durand, JiaWei He, Leonid Sigal, Greg Mori
Recently there is an increasing interest in scene generation within the research community.
no code implementations • CVPR 2019 • Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, JiaWei He, Leonid Sigal, Greg Mori
We propose a novel probabilistic generative model for action sequences.
no code implementations • CVPR 2019 • Thibaut Durand, Nazanin Mehrasa, Greg Mori
Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex.
2 code implementations • CVPR 2017 • Thibaut Durand, Taylor Mordan, Nicolas Thome, Matthieu Cord
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Ranked #3 on Weakly Supervised Object Detection on MS COCO
1 code implementation • CVPR 2016 • Thibaut Durand, Nicolas Thome, Matthieu Cord
In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON).
no code implementations • ICCV 2015 • Thibaut Durand, Nicolas Thome, Matthieu Cord
For ranking, we propose efficient solutions to exactly solve the inference and the loss-augmented problems.