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20212026
most citedSelf-supervised Contrastive Learning of Multi-view Facial Expressions

32 citations · 47 across the 20 of their papers we have counts for

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15 papers · 1 filter

cs.CV2026

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…

cs.CV2025

A Shared Encoder Approach to Multimodal Representation Learning

Shuvendu Roy, Franklin Ogidi, Ali Etemad +2

Multimodal representation learning has demonstrated remarkable potential in enabling models to process and integrate diverse data modalities, such as text and images, for improved…

cs.CV2025★ 1 cited

SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation

Shuvendu Roy, Ali Etemad

We present SelfPrompt, a novel prompt-tuning approach for vision-language models (VLMs) in a semi-supervised learning setup. Existing methods for tuning VLMs in semi-supervised set…

cs.CV2024

Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning

Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi +5

We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following…

cs.CV2024

Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models

Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour +1

We propose Consistency-guided Asynchronous Contrastive Tuning (CoACT), a novel method for continuously tuning foundation models to learn new classes in few-shot settings. CoACT con…

cs.CV2024

A Bag of Tricks for Few-Shot Class-Incremental Learning

Shuvendu Roy, Chunjong Park, Aldi Fahrezi +1

We present a bag of tricks framework for few-shot class-incremental learning (FSCIL), which is a challenging form of continual learning that involves continuous adaptation to new t…

cs.CV2023

Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition

Shuvendu Roy, Ali Etemad

Although there has been much progress in the area of facial expression recognition (FER), most existing methods suffer when presented with images that have been captured from viewi…

cs.CV2023★ 1 cited

Active Learning with Contrastive Pre-training for Facial Expression Recognition

Shuvendu Roy, Ali Etemad

Deep learning has played a significant role in the success of facial expression recognition (FER), thanks to large models and vast amounts of labelled data. However, obtaining labe…

cs.CV2023

Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data

Shuvendu Roy, Ali Etemad

Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of…

cs.CV2023★ 10 cited

Consistency-guided Prompt Learning for Vision-Language Models

Shuvendu Roy, Ali Etemad

We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models w…

cs.CV2022★ 2 cited

Impact of Strategic Sampling and Supervision Policies on Semi-supervised Learning

Shuvendu Roy, Ali Etemad

In semi-supervised representation learning frameworks, when the number of labelled data is very scarce, the quality and representativeness of these samples become increasingly impo…

cs.CV2022

Temporal Contrastive Learning with Curriculum

Shuvendu Roy, Ali Etemad

We present ConCur, a contrastive video representation learning method that uses curriculum learning to impose a dynamic sampling strategy in contrastive training. More specifically…

cs.CV2022

Analysis of Semi-Supervised Methods for Facial Expression Recognition

Shuvendu Roy, Ali Etemad

Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-a…

cs.CV2021★ 32 cited

Self-supervised Contrastive Learning of Multi-view Facial Expressions

Shuvendu Roy, Ali Etemad

Facial expression recognition (FER) has emerged as an important component of human-computer interaction systems. Despite recent advancements in FER, performance often drops signifi…

cs.CV2021

Spatiotemporal Contrastive Learning of Facial Expressions in Videos

Shuvendu Roy, Ali Etemad

We propose a self-supervised contrastive learning approach for facial expression recognition (FER) in videos. We propose a novel temporal sampling-based augmentation scheme to be u…