Papers by Joshua Feinglass
SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis (2021.acl-long)
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| Challenge: | Visual captioning is an open-ended area for evaluation, requiring specialized training to improve human-correlation. |
| Approach: | They propose a new evaluation framework rooted in information theory . they propose metric SPURTS and metric SMURF to measure fluency . |
| Outcome: | The proposed metrics achieve state-of-the-art correlation with human judgment compared with other evaluation metrics. |
TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning (2024.findings-emnlp)
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| Challenge: | Existing approaches to enhance zero-shot abilities in image captioning fail with fine-grained datasets. |
| Approach: | They propose a method to enhance captions with additional object-part details using object detector proposals and natural language processing techniques. |
| Outcome: | The proposed method improves performance on fine-grained datasets and improves on existing methods. |