no code implementations • 6 Dec 2023 • Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey, Dhruv Mahajan, Vignesh Ramanathan, Filip Radenovic
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context.
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 • 27 May 2022 • Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations.
1 code implementation • 27 May 2022 • Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan
We demonstrate by human subject evaluations that SPAMs are demonstrably more interpretable in practice, and are hence an effortless replacement for DNNs for creating interpretable and high-performance systems suitable for large-scale machine learning.
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.
1 code implementation • 17 Jun 2021 • Matthijs Douze, Giorgos Tolias, Ed Pizzi, Zoë Papakipos, Lowik Chanussot, Filip Radenovic, Tomas Jenicek, Maxim Maximov, Laura Leal-Taixé, Ismail Elezi, Ondřej Chum, Cristian Canton Ferrer
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on Image Similarity Detection on DISC21 dev
no code implementations • 24 May 2021 • Filip Radenovic, Animesh Sinha, Albert Gordo, Tamara Berg, Dhruv Mahajan
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data.
no code implementations • ECCV 2020 • Albert Gordo, Filip Radenovic, Tamara Berg
Query expansion is a technique widely used in image search consisting in combining highly ranked images from an original query into an expanded query that is then reissued, generally leading to increased recall and precision.
1 code implementation • ICCV 2019 • Giorgos Tolias, Filip Radenovic, Ondřej Chum
We show successful attacks to partially unknown systems, by designing various loss functions for the adversarial image construction.
3 code implementations • ECCV 2018 • Dmytro Mishkin, Filip Radenovic, Jiri Matas
A method for learning local affine-covariant regions is presented.
Ranked #4 on Image Matching on IMC PhotoTourism (using extra training data)
4 code implementations • NeurIPS 2017 • Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.
no code implementations • 12 Jul 2016 • Martin Cadik, Jan Vasicek, Michal Hradis, Filip Radenovic, Ondrej Chum
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.
no code implementations • CVPR 2016 • Filip Radenovic, Johannes L. Schonberger, Dinghuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
no code implementations • CVPR 2015 • Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, Jan-Michael Frahm
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
no code implementations • 13 Apr 2015 • Filip Radenovic, Herve Jegou, Ondrej Chum
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval.