Papers by Yimu Wang
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. |
Video-Text Retrieval by Supervised Sparse Multi-Grained Learning (2023.findings-emnlp)
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| Challenge: | Recent advances in video-text retrieval have led to improved representation learning methods. |
| Approach: | They propose a multi-grained sparse learning framework to learn an aligned sparsen space shared between video and text for video-text retrieval. |
| Outcome: | The proposed framework is superior to existing methods on video-text retrieval benchmarks. |
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models (2025.naacl-long)
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| Challenge: | Recent advances in video-text retrieval models have limited training data annotations. |
| Approach: | They propose a Video-Text Retrieval Paradigm with Relevance-based Augmentation which enhances video and text data using large foundation models to learn more generalized features. |
| Outcome: | The proposed method improves video-text retrieval performance over existing methods. |
ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)
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| Challenge: | Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning. |
| Approach: | They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data. |
| Outcome: | The proposed framework outperforms existing methods on video-text retrieval benchmarks without data. |
LEO-MINI: An Efficient Multimodal Large Language Model using Conditional Token Reduction and Mixture of Multi-Modal Experts (2025.emnlp-main)
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| Challenge: | Recent approaches to reduce visual tokens have been criticized for their computational efficiency and lack of visual reasoning capabilities. |
| Approach: | They propose a novel multi-modal large language model that reduces the number of visual tokens and simultaneously boosts visual reasoning capabilities. |
| Outcome: | The proposed model significantly reduces the number of visual tokens and boosts visual reasoning capabilities. |
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks (2023.emnlp-main)
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| Challenge: | a small number of gallery data points are frequently retrieved, resulting in a decline in retrieval performance. |
| Approach: | They propose a framework that leverages both gallery and query data to address hubness . they propose dual inverted softmax and dual dynamic inverted hardmax methods to normalize similarity . |
| Outcome: | The proposed framework reduces the occurrence of hubs during inference while improving similarity between non-hubs and queries. |
InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution (2023.findings-emnlp)
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| Challenge: | a recent study shows that multi-modal data representations tend to cluster within a limited convex cone, hindering retrieval performance. |
| Approach: | They propose a method that uses graph convolution and average pooling to separate representations by increasing distances between data points. |
| Outcome: | The proposed method reduces representation degeneration problem by increasing distances between data points. |