Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding (2022.findings-emnlp)
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| Challenge: | Visual scenes often involve multiple people and humans can distinguish between them based on context descriptions about what happened before, their mental/physical states, and intentions. |
| Approach: | They propose a task that tests human-centric commonsense grounding models' ability to distinguish individuals given context descriptions about what happened before and their mental/physical states or intentions. |
| Outcome: | The proposed model outperforms pre-trained and non-pretrained models on 130k commonsense descriptions annotated on 67k images. |
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| Challenge: | Visual commonsense data sets lack visual grounded representations of commonsensense . existing knowledge bases lack visual-based knowledge tied to actual visual scenes . |
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| Challenge: | Existing methods for visual grounding rely on the assumption that the given expression must be literal . this impedes the practical deployment of agents in real-world scenarios. |
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Seungju Han, Junhyeok Kim, Jack Hessel, Liwei Jiang, Jiwan Chung, Yejin Son, Yejin Choi, Youngjae Yu
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
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Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense (2022.emnlp-main)
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Grounded Multimodal Procedural Entity Recognition for Procedural Documents: A New Dataset and Baseline (2024.lrec-main)
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| Challenge: | Existing methods to extract procedural knowledge from documents focus on text-only settings, which is insufficient for entity disambiguation. |
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