Papers by Mahmoud Khademi
Multimodal Neural Graph Memory Networks for Visual Question Answering (2020.acl-main)
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| Challenge: | Visual question answering (VQA) is a new challenge for AI. |
| Approach: | They propose a graph neural network architecture based on the recently proposed Graph Network (GN) . they generate visual features and encoded captions for an image to generate two GNs . |
| Outcome: | The proposed model rivals the state-of-the-art models on Visual7W, VQA-v2.0, and CLEVR datasets. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
MM-Reasoner: A Multi-Modal Knowledge-Aware Framework for Knowledge-Based Visual Question Answering (2023.findings-emnlp)
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| Challenge: | Recent knowledge-based visual question answering approaches miss visual information captured by captions and cannot fully utilize the visual information required to answer the question. |
| Approach: | They propose a framework that extracts visual information from an image and prompts an LLM to extract query-specific knowledge from the extracted textual information. |
| Outcome: | Empirical results show that MM-Reasoner achieves state-of-the-art performance on several KVQA datasets. |
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)
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Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei
| Challenge: | Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. |
| Approach: | They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration. |
| Outcome: | The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks. |
i-Code Studio: A Configurable and Composable Framework for Integrative AI (2024.emnlp-demo)
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Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang
| Challenge: | Existing frameworks for Integrative AI lack flexibility and composability to handle multimodal tasks. |
| Approach: | They propose a configurable framework for Integrative AI that orchestrates multiple pre-trained models to conduct complex multimodal tasks. |
| Outcome: | The proposed framework achieves impressive results on zero-shot multimodal tasks . it can communicate and personalize for users, and it can be used in a multimodal agent . |