Papers with fine-grained tasks
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models (2024.findings-emnlp)
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| Challenge: | Fine-grained image classification is a challenge for vision-language models (VLMs) such as CLIP, which struggle to distinguish between semantically similar classes due to insufficient supervision for fine-grain tasks. |
| Approach: | They propose a framework that harnesses the complementary strengths of both CLIP-like and LVLMs to tackle these challenges. |
| Outcome: | The proposed framework outperforms existing models on multiple fine-grained datasets, particularly the Stanford Cars dataset. |
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification (2021.emnlp-main)
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| Challenge: | Fine-grained classification tasks involve distinguishing between classes with subtle differences between them. |
| Approach: | They analyse fine-grained text classification tasks by embedding class relationships into a contrastive objective function to help differently weigh the positives and negatives. |
| Outcome: | The proposed model outperforms previous contrastive methods on emotion classification and sentiment analysis. |
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods. |
| Approach: | They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator. |
| Outcome: | The proposed approach significantly improves the performance of existing methods. |
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
EchoMLLM: Incentivizing Echocardiographic Video Understanding with Keyframe Grounding and Report Generation (2026.findings-acl)
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Heyu Huang, Wanran Sun, Chi Chen, Bo Chen, Zonghao Guo, Yuhua Li, Ruixuan Li, Kunlun He, Maosong Sun
| Challenge: | Echocardiography analysis requires a dual capability: rigorous quantitative keyframe localization and comprehensive qualitative synthesis. |
| Approach: | They propose a unified framework designed for real-world echocardiography video understanding. |
| Outcome: | a new framework is designed to support real-world echocardiography video understanding . it reduces temporal grounding errors by up to 76% and improves report generation quality by 65% . |