Papers by Manishit Kundu

2 papers
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.
Looking Beyond the Pixels: Evaluating Visual Metaphor Understanding in VLMs (2025.findings-emnlp)

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Challenge: Visual metaphors are a complex vision–language phenomenon that requires both perceptual and conceptual reasoning to understand.
Approach: They introduce a visual metaphor dataset featuring 2177 synthetic and 350 human-annotated images and benchmark several SOTA VLMs on two tasks: Visual Metaphor Captioning (VMC) and Visual Metamorphosis VQA (VM-VQA).
Outcome: The proposed model outperforms standard few-shot baselines on visual metaphors and VM-VQA tasks.

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