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pro vyhledávání: '"Sarkar, Pritam"'
Autor:
Sarkar, Pritam, Ebrahimi, Sayna, Etemad, Ali, Beirami, Ahmad, Arık, Sercan Ö., Pfister, Tomas
Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we address object hallucinations in MLLMs, where information is generated ab
Externí odkaz:
http://arxiv.org/abs/2405.18654
The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold standard, continuous monitoring remains a challenge, leading to the ex
Externí odkaz:
http://arxiv.org/abs/2308.13568
Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, we comprehensively study the b
Externí odkaz:
http://arxiv.org/abs/2306.02014
Autor:
Sarkar, Pritam, Etemad, Ali
We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled videos. XKD is trained with two pseudo objectives. First, masked data reconstruction is performed to learn modality-specific representations from au
Externí odkaz:
http://arxiv.org/abs/2211.13929
We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects collaborate to complete a number of cogn
Externí odkaz:
http://arxiv.org/abs/2205.06887
Autor:
Sarkar, Pritam, Etemad, Ali
We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard 'synchronous' cross-modal relations, CrissCross a
Externí odkaz:
http://arxiv.org/abs/2111.05329
Autor:
Sarkar, Pritam, Lobmaier, Silvia, Fabre, Bibiana, González, Diego, Mueller, Alexander, Frasch, Martin G., Antonelli, Marta C., Etemad, Ali
Publikováno v:
Scientific Reports, December 2021
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d
Externí odkaz:
http://arxiv.org/abs/2011.02000
Autor:
Sarkar, Pritam, Etemad, Ali
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical p
Externí odkaz:
http://arxiv.org/abs/2010.00104
Autor:
Sarkar, Pritam, Etemad, Ali
We exploit a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed solution consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG
Externí odkaz:
http://arxiv.org/abs/2002.03898
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