393 citations · 621 across the 15 of their papers we have counts for
16 papers
Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs
Shayan Mohammadizadehsamakosh, Pritam Sarkar, Leonid Sigal +2
Preference optimization is increasingly used to post-train medical large vision-language models (LVLMs), yet it operates at a much coarser granularity than the one that defines cli…
OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models
Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci +6
High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers. We i…
LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
Ali Salamatian, Anthony Fuller, Pritam Sarkar +3
Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are filled with redundancy, so…
VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models
Pritam Sarkar, Ali Etemad
Despite recent advances in video understanding, the capabilities of Large Video Language Models (LVLMs) to perform video-based causal reasoning remains underexplored, largely due t…
Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization
Pritam Sarkar, Ali Etemad
Despite recent advances in Large Video Language Models (LVLMs), they still struggle with fine-grained temporal understanding, hallucinate, and often make simple mistakes on even si…
Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment
Pritam Sarkar, Sayna Ebrahimi, Ali Etemad +3
Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we add…
Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation
Debaditya Shome, Pritam Sarkar, Ali Etemad
The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold…
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Pritam Sarkar, Ahmad Beirami, Ali Etemad
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…
XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
Pritam Sarkar, Ali Etemad
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 reconstr…
AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work
Pritam Sarkar, Aaron Posen, Ali Etemad
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-conferen…
Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity
Pritam Sarkar, Ali Etemad
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 intr…
Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation Learning
Pritam Sarkar, Silvia Lobmaier, Bibiana Fabre +5
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). D…
CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG
Pritam Sarkar, Ali Etemad
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. W…
Self-supervised ECG Representation Learning for Emotion Recognition
Pritam Sarkar, Ali Etemad
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)…
Self-supervised Learning for ECG-based Emotion Recognition
Pritam Sarkar, Ali Etemad
We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transforma…
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
Pritam Sarkar, Kyle Ross, Aaron J. Ruberto +3
Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should prov…