Summary of Is ‘right’ Right? Enhancing Object Orientation Understanding in Multimodal Language Models Through Egocentric Instruction Tuning, by Ji Hyeok Jung et al.
Is ‘Right’ Right? Enhancing Object Orientation Understanding in Multimodal Language Models through Egocentric Instruction Tuning
by Ji Hyeok Jung, Eun Tae Kim, Seo Yeon Kim, Joo Ho Lee, Bumsoo Kim, Buru Chang
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 egocentric instruction tuning approach enhances the orientation understanding of multimodal large language models (MLLMs) in multimodal applications. The current MLLMs struggle with accurately interpreting object orientation in images due to inconsistent orientation annotations in training data, hindering the development of a coherent orientation understanding. To overcome this challenge, the authors generate egocentric instruction data that leverages MLLMs’ ability to recognize object details and applies prior knowledge for orientation understanding. The generated data is then used for instruction tuning to enhance the model’s capability for accurate orientation interpretation. Experimental results on the EgoOrientBench benchmark show that egocentric instruction tuning significantly improves orientation understanding without compromising overall MLLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help machines understand object orientation in images better. Currently, machines have trouble with this because they were trained using inconsistent information. To fix this, the authors create special instructions that use the machine’s ability to recognize details and prior knowledge about object orientation. These instructions are then used to train the machine to better understand object orientation. The results show that this new approach improves the machine’s understanding of object orientation without harming its overall performance. |
Keywords
» Artificial intelligence » Instruction tuning
