Zobrazeno 1 - 10
of 2 162
pro vyhledávání: '"Kalla P"'
Autor:
Kalla, Besma, Cappelletti, Martina, Hout, Menno van den, van Vliet, Vincent, Rommel, Simon, Palmieri, Luca, Bradley, Thomas, Okonkwo, Chigo
We propose an optimized optical vector network analyzer with automatic polarization control to stabilize the reference arm polarization throughout the sweep range. We demonstrate this technique, successfully removing the polarization-induced fading a
Externí odkaz:
http://arxiv.org/abs/2410.06728
Autor:
Shen, Junxiao, Boldu, Roger, Kalla, Arpit, Glueck, Michael, Karlson, Hemant Bhaskar Surale Amy
Text entry is a critical capability for any modern computing experience, with lightweight augmented reality (AR) glasses being no exception. Designed for all-day wearability, a limitation of lightweight AR glass is the restriction to the inclusion of
Externí odkaz:
http://arxiv.org/abs/2410.18100
In this work, we address the real-world, challenging task of out-of-context misinformation detection, where a real image is paired with an incorrect caption for creating fake news. Existing approaches for this task assume the availability of large am
Externí odkaz:
http://arxiv.org/abs/2410.04426
Autor:
Chudasama, Vishal, Sarkar, Hiran, Wasnik, Pankaj, Balasubramanian, Vineeth N, Kalla, Jayateja
Object detection is a critical field in computer vision focusing on accurately identifying and locating specific objects in images or videos. Traditional methods for object detection rely on large labeled training datasets for each object category, w
Externí odkaz:
http://arxiv.org/abs/2408.14249
Autor:
Kalla, Jayateja, Biswas, Soma
This paper investigates the impact of self-supervised learning, specifically image rotations, on various class-incremental learning paradigms. Here, each image with a predefined rotation is considered as a new class for training. At inference, all im
Externí odkaz:
http://arxiv.org/abs/2408.04347
We propose a novel TACLE (TAsk and CLass-awarE) framework to address the relatively unexplored and challenging problem of exemplar-free semi-supervised class incremental learning. In this scenario, at each new task, the model has to learn new classes
Externí odkaz:
http://arxiv.org/abs/2407.08041
Publikováno v:
Annals of Cardiac Anaesthesia, Vol 23, Iss 4, Pp 521-523 (2020)
Unilateral recurrent pleural effusions are commonly encountered in critical care practice. Relevant clinical history, physical examination, radiology, and diagnostic thoracentesis usually identify the cause of pleural effusion in most cases. Thoracos
Externí odkaz:
https://doaj.org/article/02966852f40e4c6b884dbb9b24b9d555
Autor:
Kalla, Jayateja, Biswas, Soma
This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catastrophic forgetting in the context of long-tailed data distributions. Ad
Externí odkaz:
http://arxiv.org/abs/2311.01227
Autor:
Christina Sternberg, Martin Raigel, Tanja Limberger, Karolína Trachtová, Michaela Schlederer, Desiree Lindner, Petra Kodajova, Jiaye Yang, Roman Ziegler, Jessica Kalla, Stefan Stoiber, Saptaswa Dey, Daniela Zwolanek, Heidi A. Neubauer, Monika Oberhuber, Torben Redmer, Václav Hejret, Boris Tichy, Martina Tomberger, Nora S. Harbusch, Jan Pencik, Simone Tangermann, Vojtech Bystry, Jenny L. Persson, Gerda Egger, Sarka Pospisilova, Robert Eferl, Peter Wolf, Felix Sternberg, Sandra Högler, Sabine Lagger, Stefan Rose-John, Lukas Kenner
Publikováno v:
Molecular Cancer, Vol 23, Iss 1, Pp 1-22 (2024)
Abstract Background Prostate cancer ranks as the second most frequently diagnosed cancer in men worldwide. Recent research highlights the crucial roles IL6ST-mediated signaling pathways play in the development and progression of various cancers, part
Externí odkaz:
https://doaj.org/article/b138c0eace4d4d388c5bcd9f679c1598
Autor:
Kalla, Jayateja, Biswas, Soma
Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes. FSCIL suffers from two major challenges: (i) over-fitting on the new
Externí odkaz:
http://arxiv.org/abs/2307.02246