Papers with description generation
Improving Image Captioning via Predicting Structured Concepts (2023.emnlp-main)
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| Challenge: | Existing studies on image captioning ignore the relationship between concepts . current methods for image caption generation ignore this relationship . |
| Approach: | They propose a structured concept predictor to predict concepts and their structures . they integrate these predictions into captioning to enhance visual signals . |
| Outcome: | The proposed approach improves image captioning performance by using semantic concepts as a bridge between images and texts. |
An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
| Approach: | They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale . |
| Outcome: | The proposed method significantly improves the performance on commonsense generation tasks. |
Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)
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| Challenge: | YManga dataset is the first specifically designed for yonkoma manga understanding . |
| Approach: | They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels. |
| Outcome: | The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations. |
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models (2025.coling-main)
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Kazuki Hayashi, Kazuma Onishi, Toma Suzuki, Yusuke Ide, Seiji Gobara, Shigeki Saito, Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
| Challenge: | Large-scale vision language models excel at generating factual content, but their ability to rank images from multiple perspectives has not been explored. |
| Approach: | They propose a framework to evaluate large-scale vision-language models by measuring their ability to rank image texts from multiple perspectives. |
| Outcome: | The proposed evaluation framework measures how closely LVLMs' judgments align with human interpretations. |
NBDESCRIB: A Dataset for Text Description Generation from Tables and Code in Jupyter Notebooks with Guidelines (2025.findings-acl)
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| Challenge: | Existing methods for Jupyter Notebooks focus on generating cell-level descriptions from code snippets or table outputs independently. |
| Approach: | They propose a task to generate personalized cell-level descriptions using code, tables, and user-written guidelines in Jupyter Notebooks. |
| Outcome: | The proposed task combines code, tables, and user-written guidelines with personalized descriptions to evaluate the performance of existing models. |
MotifAgent: Learning Molecular Assembly through Multi-Agent Collaboration for Chemical Language Understanding (2026.findings-acl)
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| Challenge: | Existing approaches to molecular understanding are limited to static motif recognition without understanding connection rules governing how motifs assemble into valid topological structures. |
| Approach: | They propose a multi-agent reinforcement learning framework inspired by emergent collective intelligence to solve a problem where each motif is represented by an agent sharing a common LLM backbone. |
| Outcome: | Extensive experiments show that the proposed framework surpasses specialized expert models in molecular understanding tasks. |