no code implementations • 17 Dec 2024 • Yash Patel, Giorgos Tolias, Jiri Matas
This paper addresses supervised deep metric learning for open-set image retrieval, focusing on three key aspects: the loss function, mixup regularization, and model initialization.
no code implementations • 14 Oct 2024 • Xueying Tian, Yash Patel, Yue Wang
Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions.
1 code implementation • 23 Jun 2024 • Mrinal Kanti Dhar, Chuanbo Wang, Yash Patel, Taiyu Zhang, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Keke Chen, Zeyun Yu
Additionally, our hybrid model outperforms the state-of-the-art methods with a 92. 99% DSC in performing binary segmentation of DFU wound areas when tested on the Chronic Wound dataset.
no code implementations • 25 May 2024 • Yash Patel, Sahana Rayan, Ambuj Tewari
To discover designs that perform well even under the misspecification of system dynamics, such end-to-end pipelines have now begun evaluating designs with a robust control objective in place of the nominal optimal control setup.
no code implementations • 25 May 2024 • Eduardo Ochoa Rivera, Yash Patel, Ambuj Tewari
Distribution-free uncertainty estimation for ensemble methods is increasingly desirable due to the widening deployment of multi-modal black-box predictive models.
no code implementations • 16 Oct 2023 • Yash Patel, Sahana Rayan, Ambuj Tewari
Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings.
1 code implementation • 23 Aug 2023 • Yash Patel, Tirth Shah, Mrinal Kanti Dhar, Taiyu Zhang, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments.
1 code implementation • 20 Jul 2023 • Zhiwei Xue, Yuhang Li, Yash Patel, Jeffrey Regier
As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images.
1 code implementation • 23 May 2023 • Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari
We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.
1 code implementation • 4 May 2023 • Mrinal Kanti Dhar, Taiyu Zhang, Yash Patel, Sandeep Gopalakrishnan, Zeyun Yu
As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE.
no code implementations • 3 May 2023 • Yash Patel
An appropriate proxy has to be designed for a novel task, which may not be feasible for a non-specialist.
1 code implementation • 11 Feb 2023 • Štěpán Šimsa, Milan Šulc, Michal Uřičář, Yash Patel, Ahmed Hamdi, Matěj Kocián, Matyáš Skalický, Jiří Matas, Antoine Doucet, Mickaël Coustaty, Dimosthenis Karatzas
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition.
no code implementations • 7 Feb 2023 • Yash Patel, Yusheng Xie, Yi Zhu, Srikar Appalaraju, R. Manmatha
Instead of purely relying on the alignment from the noisy data, this paper proposes a novel loss function termed SimCon, which accounts for intra-modal similarities to determine the appropriate set of positive samples to align.
no code implementations • 29 Jan 2023 • Štěpán Šimsa, Milan Šulc, Matyáš Skalický, Yash Patel, Ahmed Hamdi
The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from business documents.
no code implementations • 21 Jan 2023 • Hosein Barzekar, Yash Patel, Ling Tong, Zeyun Yu
Cancer is a leading cause of death in many countries.
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.
2 code implementations • ICCV 2023 • Tong Wei, Yash Patel, Alexander Shekhovtsov, Jiri Matas, Daniel Barath
We propose $\nabla$-RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline.
1 code implementation • 19 Nov 2022 • Yash Patel, Ambuj Tewari
The generation of conformers has been a long-standing interest to structural chemists and biologists alike.
no code implementations • 7 Nov 2022 • Rahaf Aljundi, Yash Patel, Milan Sulc, Daniel Olmeda, Nikolay Chumerin
In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss.
no code implementations • 17 Apr 2022 • D. M. Anisuzzaman, Yash Patel, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red.
no code implementations • 14 Sep 2021 • D. M. Anisuzzaman, Yash Patel, Behrouz Rostami, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers.
2 code implementations • CVPR 2022 • Yash Patel, Giorgos Tolias, Jiri Matas
This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach.
Ranked #1 on
Vehicle Re-Identification
on VehicleID Small
no code implementations • 8 Mar 2021 • Yash Patel, Jiri Matas
This paper proposes a procedure to train a scene text recognition model using a robust learned surrogate of edit distance.
no code implementations • 22 Oct 2020 • Slobodan Djukanović, Yash Patel, Jiři Matas, Tuomas Virtanen
This distance is predicted from audio using a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs).
1 code implementation • 15 Sep 2020 • D. M. Anisuzzaman, Yash Patel, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system.
no code implementations • ECCV 2020 • Yash Patel, Tomas Hodan, Jiri Matas
The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model is tuned using the learned surrogate.
no code implementations • 12 Feb 2020 • Yash Patel, Srikar Appalaraju, R. Manmatha
The proposed compression model incorporates the salient regions and optimizes on the proposed perceptual similarity metric.
no code implementations • 9 Aug 2019 • Yash Patel, Srikar Appalaraju, R. Manmatha
Recently, there has been much interest in deep learning techniques to do image compression and there have been claims that several of these produce better results than engineered compression schemes (such as JPEG, JPEG2000 or BPG).
no code implementations • 18 Jul 2019 • Yash Patel, Srikar Appalaraju, R. Manmatha
In several cases, the MS-SSIM for deep learned techniques is higher than say a conventional, non-deep learned codec such as JPEG-2000 or BPG.
no code implementations • 1 Jul 2019 • Nibal Nayef, Yash Patel, Michal Busta, Pinaki Nath Chowdhury, Dimosthenis Karatzas, Wafa Khlif, Jiri Matas, Umapada Pal, Jean-Christophe Burie, Cheng-Lin Liu, Jean-Marc Ogier
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense.
Cultural Vocal Bursts Intensity Prediction
General Classification
+2
no code implementations • 31 Jan 2019 • Yash Patel, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places.
1 code implementation • 4 Jul 2018 • Yash Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration.
no code implementations • 19 May 2018 • Yash Patel, Kashyap Chitta, Bhavan Jasani
We address the problem of semi-supervised domain adaptation of classification algorithms through deep Q-learning.
3 code implementations • 30 Jan 2018 • Michal Bušta, Yash Patel, Jiri Matas
An end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed.
no code implementations • CVPR 2017 • Lluis Gomez, Yash Patel, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible.