Papers with action understanding
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments (2022.coling-1)
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| Challenge: | embodied AI tasks require a strong understanding of verbs and their corresponding actions. |
| Approach: | They propose a controlled benchmark for embodied action understanding using a simulated environment and a visual feature extractor. |
| Outcome: | The proposed benchmark achieves 81.4% accuracy and high inter-annotator agreement . the proposed model falls behind human models in a zero-shot scenario . |
OSCBench: Benchmarking Object State Change in Text-to-Video Generation (2026.acl-long)
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Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen
| Challenge: | Existing benchmarks focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding unexplored. |
| Approach: | They introduce a benchmark specifically designed to assess OSC performance in T2V models. |
| Outcome: | The proposed benchmark assesses the performance of open-source and proprietary T2V models on object state change (OSC) in the context of novel and compositional scenarios. |