Papers by Md. Atabuzzaman
Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated impressive performance on vision-language reasoning tasks, but their potential for zero-shot fine-grained image classification remains underexplored. |
| Approach: | They propose a method that transforms zero-shot fine-grained image classification into a visual question-answering framework. |
| Outcome: | The proposed method outperforms the current state-of-the-art approach and outperformed existing methods. |
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing work has explored unimodal biases in visual question answering, but the problem of selection bias in Multiple-Choice Question Answering (MCQA) remains underexplored. |
| Approach: | They propose a method that mitigates bias without retraining and is compatible with frozen LVLMs. |
| Outcome: | The proposed method mitigates bias without retraining and is compatible with frozen LVLMs. |