Gabriel Oliveira dos Santos

Also published as: Gabriel Oliveira dos Santos


2023

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CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages
Gabriel Oliveira dos Santos | Diego Alysson Braga Moreira | Alef Iury Ferreira | Jhessica Silva | Luiz Pereira | Pedro Bueno | Thiago Sousa | Helena Maia | Nádia Da Silva | Esther Colombini | Helio Pedrini | Sandra Avila
Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)

2021

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CIDEr-R: Robust Consensus-based Image Description Evaluation
Gabriel Oliveira dos Santos | Esther Luna Colombini | Sandra Avila
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high sentence length variance. We demonstrate that CIDEr-R is more accurate and closer to human judgment than CIDEr-D; CIDEr-R is more robust regarding the number of available references. Our results reveal that using Self-Critical Sequence Training to optimize CIDEr-R generates descriptive captions. In contrast, when CIDEr-D is optimized, the generated captions’ length tends to be similar to the reference length. However, the models also repeat several times the same word to increase the sentence length.