Papers by Prajwal Gatti
COFAR: Commonsense and Factual Reasoning in Image Search (2022.aacl-main)
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Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani
| Challenge: | Existing approaches to retrieve relevant images for natural language searches are limited by visual recognition and lack of commonsense reasoning. |
| Approach: | They propose a framework that leverages visual content and natural language queries to enable commonsense reasoning and factual reasoning in the image search. |
| Outcome: | The proposed framework enables commonsense and factual reasoning in image search on a COFAR dataset. |
VisToT: Vision-Augmented Table-to-Text Generation (2022.emnlp-main)
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| Challenge: | Existing models for data-to-text generation are wrongly generating estate in the output text. |
| Approach: | They propose a task that incorporates visual cues from tables and associated images to generate relevant text. |
| Outcome: | The proposed task incorporates visual cues from tables and associated images to generate relevant text. |