Summary of Oscar: Object State Captioning and State Change Representation, by Nguyen Nguyen et al.
OSCaR: Object State Captioning and State Change Representation
by Nguyen Nguyen, Jing Bi, Ali Vosoughi, Yapeng Tian, Pooyan Fazli, Chenliang Xu
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Object State Captioning and State Change Representation (OSCaR) dataset and benchmark aim to improve the understanding of complex visual environments by evaluating multimodal large language models (MLLMs). The OSCaR dataset consists of 14,084 annotated video segments featuring nearly 1,000 unique objects from various egocentric video collections. While MLLMs demonstrate some skill, they lack a comprehensive grasp of object state changes. To address this challenge, the paper proposes a fine-tuned model that requires significant improvements in accuracy and generalization ability for effective comprehension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this research is to help AI models better understand how objects change in real-world settings. Currently, AI has trouble describing complex scenes, identifying moving objects, and understanding what’s happening through language. This paper creates a new dataset called OSCaR that helps test large language models’ ability to understand these changes. The dataset includes lots of video clips with different objects and actions. While the AI models can do some things right, they still struggle to fully grasp how objects change. |
Keywords
* Artificial intelligence * Generalization
