Summary of Deepseek-vl: Towards Real-world Vision-language Understanding, by Haoyu Lu et al.
DeepSeek-VL: Towards Real-World Vision-Language Understanding
by Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 DeepSeek-VL model is an open-source vision-language architecture optimized for real-world applications. By integrating computer vision and natural language processing techniques, the model addresses the challenges of complex visual content analysis and language-based understanding. The approach relies on three interdependent dimensions: (1) multimodal feature extraction, (2) hierarchical reasoning mechanisms, and (3) task-specific adaptation strategies. Experimental results demonstrate the effectiveness of DeepSeek-VL in various applications, including image captioning, visual question answering, and visual grounding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DeepSeek-VL is a new type of computer model that helps us understand images and words better. It’s like having a superpower that lets you read pictures! The model has three main parts: it extracts information from images and text, uses that info to make smart decisions, and adjusts its thinking to fit different tasks. This means DeepSeek-VL can help with things like describing what’s in a photo or answering questions about an image. |
Keywords
» Artificial intelligence » Feature extraction » Grounding » Image captioning » Natural language processing » Question answering
