Summary of Sq-llava: Self-questioning For Large Vision-language Assistant, by Guohao Sun et al.
SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
by Guohao Sun, Can Qin, Jiamian Wang, Zeyuan Chen, Ran Xu, Zhiqiang Tao
First submitted to arxiv on: 17 Mar 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 recent advancements in vision-language models have led to notable generalization in broad tasks through visual instruction tuning. However, bridging the gap between the pre-trained vision encoder and large language models (LLMs) has become the bottleneck for the whole network. This paper introduces a novel framework named SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant that harnesses the overlooked context within visual instruction data to train the model to self-supervised “learning” how to ask high-quality questions. SQ-LLaVA exhibits proficiency in generating flexible and meaningful image-related questions while analyzing the visual clue and prior language knowledge, signifying an advanced level of generalized visual understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine that can look at pictures and ask smart questions about what it sees! This is exactly what this new framework, called SQ-LLaVA, does. By teaching itself to ask good questions, SQ-LLaVA gets better at understanding images in different contexts. It’s like training a super-smart AI that can analyze pictures and come up with clever questions about what it sees! |
Keywords
* Artificial intelligence * Encoder * Generalization * Instruction tuning * Self supervised
