Summary of Instruction-guided Visual Masking, by Jinliang Zheng et al.
Instruction-Guided Visual Masking
by Jinliang Zheng, Jianxiong Li, Sijie Cheng, Yinan Zheng, Jiaming Li, Jihao Liu, Yu Liu, Jingjing Liu, Xianyuan Zhan
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 This paper introduces Instruction-guided Visual Masking (IVM), a new model that improves multimodal instruction following by creating visual masks for irrelevant image regions. IVM is designed to work with various multimodal models, such as LLM and robot models, and enhances their performance on tasks like VQA and embodied robotic control. The authors create a dataset, IVM-Mix-1M, with 1 million image-instruction pairs and develop a new learning technique, Discriminator Weighted Supervised Learning (DWSL), to train IVM effectively. Experimental results show that IVM significantly boosts the performance of diverse multimodal models on challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines better understand instructions by looking at pictures. Imagine you’re giving a robot instructions, but it’s not sure what part of the picture is important. This new model, called Instruction-guided Visual Masking (IVM), helps the robot focus on the right parts of the picture to follow your instructions correctly. The researchers created a big dataset with many examples and developed a special way to train IVM to make it work well. They tested IVM with different machines and found that it helped them do better on tricky tasks. |
Keywords
» Artificial intelligence » Supervised
