Papers by Chong Shen
Combining Tradition with Modernness: Exploring Event Representations in Vision-and-Language Models for Visual Goal-Step Inference (2023.acl-srw)
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| Challenge: | Existing methods for representing procedural knowledge are limited to capturing the most crucial information, namely actions and the participants, to learn stereotypical event sequences. |
| Approach: | They propose a task that uses images to identify steps towards achieving a goal in the multimodal domain. |
| Outcome: | The proposed task uses images that represent steps towards achieving a textually expressed goal in the multimodal domain. |
From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)
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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu
| Challenge: | Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning. |
| Approach: | They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model. |
| Outcome: | The proposed method improves egocentric reasoning abilities on six tasks. |
Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)
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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang
| Challenge: | PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations . |
| Approach: | They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations . |
| Outcome: | The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD. |
DocFusion: A Unified Framework for Document Parsing Tasks (2025.findings-acl)
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Mingxu Chai, Ziyu Shen, Chong Zhang, Yue Zhang, Xiao Wang, Shihan Dou, Jihua Kang, Jiazheng Zhang, Qi Zhang
| Challenge: | Existing methods for document parsing often employ multiple models, limiting performance . Existing models often employ discrete tokens, whereas recognition relies on continuous coordinates . |
| Approach: | They propose a Gaussian-Kernel Cross-Entropy Loss (GK-CEL) that unifies detection and recognition by enabling generative frameworks to handle both tasks simultaneously. |
| Outcome: | The proposed model performs competitively across four core document parsing tasks. |