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SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation
arXiv – CS AI|Zisheng Chen, Chunwei Wang, Runhui Huang, Hongbin Xu, Xiuwei Chen, Jun Zhou, Jianhua Han, Hang Xu, Xiaodan Liang||2 views
🤖AI Summary
Researchers introduce SemHiTok, a unified image tokenizer that uses semantic-guided hierarchical codebooks to balance multimodal understanding and generation tasks. The system decouples semantic and pixel features through a novel architecture that builds pixel sub-codebooks on pretrained semantic codebooks, achieving superior performance in both image reconstruction and multimodal understanding.
Key Takeaways
- →SemHiTok addresses the challenge of balancing high-level semantic features for understanding with low-level pixel features for generation.
- →The system uses a semantic-guided hierarchical codebook that decouples semantic and pixel features structurally and in training strategy.
- →Experiments show leading performance in image reconstruction and multimodal understanding under LLaVA-v1.5 settings.
- →The unified MLLM built with SemHiTok demonstrates superior performance across both understanding and generation tasks.
- →The architecture achieves better trade-offs compared to previous joint training methods for unified image tokenizers.
#semhitok#image-tokenizer#multimodal#ai-research#computer-vision#hierarchical-codebook#semantic-understanding#image-generation#mllm
Read Original →via arXiv – CS AI
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