Papers by Biplab Banerjee
CaRVE: Critiquing and Refining Visual Elaborations for Figurative Language Illustrations (2026.findings-acl)
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| Challenge: | Existing text-to-image frameworks for figurative illustration rely on proprietary models or human supervision to achieve adequate alignment. |
| Approach: | They propose a critique-driven framework that uses VLM feedback to refine visual elaborations for figurative image generation. |
| Outcome: | The proposed framework outperforms existing figurative image-to-text pipelines on human-supervised visual elaborations. |
“My life is miserable, have to sign 500 autographs everyday”: Exposing Humblebragging, the Brags in Disguise (2025.findings-acl)
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| Challenge: | Humblebragging is a phenomenon in which individuals present self-promotional statements under the guise of modesty or complaints. |
| Approach: | They propose a task of automatically detecting humblebragging in text and propose '4-tuple definition' they also propose machine learning, deep learning, and large language models to perform the task . |
| Outcome: | The proposed model achieves an F1-score of 0.88 and is non-trivial even for humans. |
ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media (2025.emnlp-main)
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Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, Pushpak Bhattacharyya
| Challenge: | Almost 50% of depression patients face the risk of going into relapse. |
| Approach: | They propose to validate a social media dataset on depression relapse using cognitive theories of depression. |
| Outcome: | The first clinically validated social media dataset focused on depression relapse comprises 204 Reddit users annotated by mental health professionals. |
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs (2026.findings-acl)
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| Challenge: | Pretrained biomedical vision–language models perform well on average but often degrade on challenging modalities. |
| Approach: | They propose a prompt-learning framework that improves cross-domain generalization without extensive backbone fine-tuning. |
| Outcome: | BioVLM learns a diverse prompt bank and introduces dynamic prompt selection . it can combine sparse few-shot evidence with rich LLM semantic priors . bioVLM achieves state-of-the-art on 11 MedMNIST+ 2D datasets based on the proposed framework . |