Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals (2022.acl-long)
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| Challenge: | Current machine learning methods are incapable of efficiently utilizing multimodal information. |
| Approach: | They propose to use text-and-image alignment to improve machine learning's performance on multimodal event sequencing. |
| Outcome: | The proposed models perform significantly worse than humans on multimodal event sequencing than humans. |
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| Challenge: | a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions. |
| Approach: | They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe. |
| Outcome: | The proposed algorithm learns pairwise alignments between different recipes for the same dish. |
Recognizing Multimodal Entailment (2021.acl-tutorials)
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Cesar Ilharco, Afsaneh Shirazi, Arjun Gopalan, Arsha Nagrani, Blaz Bratanic, Chris Bregler, Christina Funk, Felipe Ferreira, Gabriel Barcik, Gabriel Ilharco, Georg Osang, Jannis Bulian, Jared Frank, Lucas Smaira, Qin Cao, Ricardo Marino, Roma Patel, Thomas Leung, Vaiva Imbrasaite
| Challenge: | This tutorial introduces the multimodal entailment task for detecting semantic alignments . the task requires fine-grained understanding of visual and linguistic semantics questions . |
| Approach: | This tutorial introduces the multimodal entailment task to machine learning . it introduces a dataset for recognizing multimodal alignments . |
| Outcome: | This tutorial introduces the multimodal entailment task . it can be useful for detecting semantic alignments when a single modality alone is not enough . |
Grounding Partially-Defined Events in Multimodal Data (2024.findings-emnlp)
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Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme
| Challenge: | Evidence suggests prelinguistic infants are capable of recognizing discrete events in real-world stimuli. |
| Approach: | They propose a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. |
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Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (2025.findings-emnlp)
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| Challenge: | Existing retrieval methods neglect the execution sequence structures inherent in procedural documents. |
| Approach: | They propose a retrieval model which integrates procedural graphs with document representations. |
| Outcome: | The proposed model integrates procedural graphs with document representations to improve document retrieval. |
Multimodal Intent Discovery from Livestream Videos (2022.findings-naacl)
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Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
| Challenge: | Existing models for instructional video understanding struggle to understand abstract intents . identifying procedural intent within instructional videos is a challenging task . |
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| Outcome: | The proposed model improves on baseline models and compares it to existing models. |
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
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Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences? (2025.findings-acl)
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| Challenge: | Existing benchmarks focus on single image settings, but some focus on multi-image settings. |
| Approach: | They introduce the TempVS benchmark which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models in image sequences. |
| Outcome: | The proposed model performs poorly compared to human models in vision and language tasks. |
Order-Based Pre-training Strategies for Procedural Text Understanding (2024.naacl-short)
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| Challenge: | Procedural text is difficult to understand due to the changing attributes of entities in the context. |
| Approach: | They propose sequence-based pre-training methods to enhance procedural understanding in natural language processing by using ordered instructions to guide individuals through a task. |
| Outcome: | The proposed methods improve on two datasets in the datasets NPN-Cooking and ProPara domains respectively. |
ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding (2025.naacl-long)
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Kimihiro Hasegawa, Wiradee Imrattanatrai, Zhi-Qi Cheng, Masaki Asada, Susan Holm, Yuran Wang, Ken Fukuda, Teruko Mitamura
| Challenge: | Existing studies typically provide traditional, but less practical evaluation testbeds for multimodal systems. |
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| Outcome: | The proposed evaluation dataset reveals a significant gap between human and competitive multimodal models. |
Knowledge-Aware Reasoning over Multimodal Semi-structured Tables (2024.findings-emnlp)
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Suyash Mathur, Jainit Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth
| Challenge: | Existing datasets for tabular question answering focus on text within cells, but real-world data is multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content. |
| Approach: | They propose a dataset to assess whether current AI models can perform knowledge-aware reasoning on multimodal structured data. |
| Outcome: | The proposed dataset is a robust benchmark for advancing AI’s comprehension and capabilities in analyzing multimodal structured data. |