Papers by Abisek Rajakumar Kalarani
A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection (2023.findings-acl)
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| Challenge: | Existing approaches to detect metaphor and hyperbole independently have not explored their relationship computationally. |
| Approach: | They propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously by annotating two hyperbolic datasets with metaphor labels. |
| Outcome: | The proposed framework improves state-of-the-art hyperbole detection by 12% over existing methods. |
“Let’s not Quote out of Context”: Unified Vision-Language Pretraining for Context Assisted Image Captioning (2023.acl-industry)
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| Challenge: | Large enterprises have several teams to create their content for the purpose of marketing, campaigning, or even maintaining a brand presence. |
| Approach: | They propose a new unified Vision-Language (VL) model with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. |
| Outcome: | The proposed model achieves state-of-the-art with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. |
Unveiling the Invisible: Captioning Videos with Metaphors (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that Vision-Language models cannot understand visual metaphors in memes and adverts. |
| Approach: | They propose a task to describe visual metaphors in videos using a manually created dataset and a new metric called Average Concept Distance to automatically evaluate creativity. |
| Outcome: | The proposed system performs comparable to existing video language models on the proposed task and can be used for future research. |