Papers with IC datasets
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. |
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification (2023.emnlp-main)
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Mujeen Sung, James Gung, Elman Mansimov, Nikolaos Pappas, Raphael Shu, Salvatore Romeo, Yi Zhang, Vittorio Castelli
| Challenge: | Existing methods for IC training do not provide sufficient examples for each intent . a novel pre-training method is proposed to provide a better understanding of intents . |
| Approach: | They propose a method that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks. |
| Outcome: | The proposed method achieves 5.4% and 4.0% higher accuracy than the current state-of-the-art method on four IC datasets. |