Papers by Wanrong Zhu
End-to-end Dense Video Captioning as Sequence Generation (2022.coling-1)
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| Challenge: | Existing methods for dense video captioning use a two-stage generative process . but, more complex tasks are not able to fully utilize this powerful paradigm . |
| Approach: | They propose to model two subtasks of dense video captioning as one sequence generation task and predict the events and the corresponding descriptions. |
| Outcome: | Experiments on YouCook2 and ViTT show that the proposed model can be used on any video platform. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
Diagnosing Vision-and-Language Navigation: What Really Matters (2022.naacl-main)
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Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Wang, Qi Wu, Miguel Eckstein, William Yang Wang
| Challenge: | Existing models claim to be able to align object tokens with specific visual targets, but there are non-negligible gaps between the two. |
| Approach: | They conduct diagnostic experiments to examine how the agents perceive multimodal input by ablation diagnostics input data. |
| Outcome: | The results show that indoor and outdoor navigation agents refer to object and direction tokens when making decisions. |
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations (2020.emnlp-main)
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| Challenge: | A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. |
| Approach: | They propose to use visual attention to build robust benchmark datasets and models that can generalize well in real-world settings. |
| Outcome: | The proposed models show that human-generated references vary drastically in different datasets/tasks, revealing the nature of each task. |
Multimodal Procedural Planning via Dual Text-Image Prompting (2024.findings-emnlp)
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| Challenge: | Embodied agents have demonstrated performance in following instructions informed by texts and images . however, the potential of models providing useful guidelines for humans to complete tasks remains underexplored . |
| Approach: | They propose a multimodal procedural planning task that generates paired text-image plans . this task provides more complementary and informative guidance than unimodal plans a . authors propose modality prompting methods that leverage zero-shot reasoning ability . |
| Outcome: | The proposed method improves the interaction in dual modalities and provides more information than unimodal plans. |
Imagination-Augmented Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing methods for Natural Language Understanding focus on textual signals, which hinders models from learning efficiently from limited data samples. |
| Approach: | They propose an Imagination-Augmented Cross-modal Encoder to solve natural language understanding tasks from a novel learning perspective. |
| Outcome: | The proposed learning paradigm bridges the gap between human and agent language understanding in both linguistic and perceptual procedures. |
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation (2023.findings-eacl)
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| Challenge: | Existing evaluation methods for natural language generation rely on token-level or embedding-level comparisons with text references. |
| Approach: | They propose to use text-to-image generator to generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings. |
| Outcome: | The proposed metric improves existing evaluation metrics’ correlations with human similarity judgments in both reference-based and reference-free scenarios. |
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation (2023.emnlp-main)
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| Challenge: | Experimental results show that GPT-k models focus more on inserting modifiers than predicting spontaneous changes in the primary subject matter. |
| Approach: | They compare the common edits made by humans and GPT-k models to examine their performance in prompting T2I. |
| Outcome: | The proposed models improve the prompt editing process by 20-30%, the authors show . they show that humans tend to replace words and phrases with modifiers . |
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
TokenShapley: Token Level Context Attribution with Shapley Value (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge. |
| Approach: | They propose a token-level attribution method that combines Shapley value-based data attribution with KNN-based retrieval techniques to improve attribution accuracy. |
| Outcome: | TokenShapley outperforms state-of-the-art methods on four benchmarks . it achieves an 11–23% improvement in accuracy on the benchmarks. |
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation (2021.eacl-main)
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Wanrong Zhu, Xin Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
| Challenge: | Outdoor vision-and-language navigation (VLN) tasks require visual grounding to generate correct actions. |
| Approach: | They propose a multimodal text style transfer learning approach to mitigate data scarcity in outdoor vision-and-language navigation tasks. |
| Outcome: | The proposed approach outperforms baseline models on the outdoor vision-and-language navigation task, improving task completion rate by 8.7% relative to the baseline models. |