Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models

• URL: http://arxiv.org/abs/2401.00127v1
• Date: Sat, 30 Dec 2023 03:19:54 GMT
• Title: Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models
• Authors: Ashhadul Islam, Md. Rafiul Biswas, Wajdi Zaghouani, Samir Brahim Belhaouari, Zubair Shah
• Abstract summary: Large Language and Vision Assistant models (LLVAs) engage users in rich conversational experiences intertwined with image-based queries. This paper takes a unique perspective on LMMs, exploring their efficacy in performing image classification tasks using tailored prompts. Our study includes a benchmarking analysis across four diverse datasets: MNIST, Cats Vs. Dogs, Hymnoptera (Ants Vs. Bees), and an unconventional dataset comprising Pox Vs. Non-Pox skin images.
• Score: 0.09264362806173355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: $ $The synergy of language and vision models has given rise to Large Language and Vision Assistant models (LLVAs), designed to engage users in rich conversational experiences intertwined with image-based queries. These comprehensive multimodal models seamlessly integrate vision encoders with Large Language Models (LLMs), expanding their applications in general-purpose language and visual comprehension. The advent of Large Multimodal Models (LMMs) heralds a new era in Artificial Intelligence (AI) assistance, extending the horizons of AI utilization. This paper takes a unique perspective on LMMs, exploring their efficacy in performing image classification tasks using tailored prompts designed for specific datasets. We also investigate the LLVAs zero-shot learning capabilities. Our study includes a benchmarking analysis across four diverse datasets: MNIST, Cats Vs. Dogs, Hymnoptera (Ants Vs. Bees), and an unconventional dataset comprising Pox Vs. Non-Pox skin images. The results of our experiments demonstrate the model's remarkable performance, achieving classification accuracies of 85\%, 100\%, 77\%, and 79\% for the respective datasets without any fine-tuning. To bolster our analysis, we assess the model's performance post fine-tuning for specific tasks. In one instance, fine-tuning is conducted over a dataset comprising images of faces of children with and without autism. Prior to fine-tuning, the model demonstrated a test accuracy of 55\%, which significantly improved to 83\% post fine-tuning. These results, coupled with our prior findings, underscore the transformative potential of LLVAs and their versatile applications in real-world scenarios.

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