HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |
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How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
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Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains. |
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| Challenge: | CLIP revolutes vision-language pretraining by using contrastive learning on paired web data. |
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MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization. |
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Which Modality should I use - Text, Motif, or Image? : Understanding Graphs with Large Language Models (2024.findings-naacl)
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| Challenge: | Current research typically employs limited setups with small real-world graphs. |
| Approach: | They propose a new approach to encoding a graph with diverse modalities, such as text, image, and motif, coupled with prompts to approximate a diagram’s global connectivity. |
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Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
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Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
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Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2020.emnlp-main)
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| Challenge: | Text classification is a critical research topic with broad applications in natural language processing. graph neural networks (GNNs) have received increasing attention but their performance is jeopardized in practice. |
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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (N18-2)
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| Challenge: | Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. |
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