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Published Paper Details:
Paper Title
Enhancing Contextual Emotion Recognition Using Large Vision-Language Models: A Review
Authors
Vaishnavi Chevale, Santosh Gaikwad, Arshiya Khan, R.S. Deshpande
Keywords
Contextual Emotion Recognition * Vision-Language Models * Multimodal Emotion Analysis * CLIP (Contrastive Language-Image Pretraining) * Deep Learning * Human-Computer Interaction * Affective Computing * Sentiment Analysis * Visual-Linguistic Fusion * Emotion Detection * Multimodal Learning * Large Language Models (LLMs) * Natural Language Processing (NLP) * Computer Vision * Transformer Models
Abstract
Emotion recognition has become increasingly relevant in human-computer interaction, mental health assessment,
and social robotics. Traditional models often rely on facial expressions or speech to identify emotions, ignoring the contextual subtleties that drive real emotional states. The emergence of Large
Vision-Language Models (VLMs), which integrate multi-modal
input to derive semantic understanding, presents an opportunity
to enhance the performance and contextual sensitivity of emotion
recognition systems. This review paper explores the role of
VLMs in contextual emotion recognition, discussing foundational
architectures like CLIP, Flamingo, and GPT-Vision hybrids,
and their application to understanding emotion in visual and
linguistic settings. We also investigate key challenges, datasets,
metrics, and future prospects in building robust, real-time, and
ethically grounded emotion recognition models
IJCRT's Publication Details
Unique Identification Number - IJCRT2506657
Paper ID - 289153
Page Number(s) - f635-f637
Pubished in - Volume 13 | Issue 6 | June 2025
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Vaishnavi Chevale, Santosh Gaikwad, Arshiya Khan, R.S. Deshpande, "Enhancing Contextual Emotion Recognition Using Large Vision-Language Models: A Review", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.f635-f637, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506657.pdf
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Volume 13 | Issue 12
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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