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About this paper
Shengqiong Wu, Hao Fei, Xiangtai Li, Jiayi Ji, Hanwang Zhang, Tat-Seng Chua, Shuicheng Yan
This research introduces SeTok, a Semantic-Equivalent Vision Tokenizer, and integrates it into SETOKIM, a Multimodal Large Language Model, to generate semantically complete visual tokens that dynamically adapt to image content. The approach yields superior performance across tasks like visual understanding, generation, editing, and referring segmentation by enhancing fine-grained vision-language alignment.
Key insights
- Dynamic clustering is crucial for generating semantically complete tokens and adapting to image complexity, leading to fewer tokens on average and reduced computational costs.
- Explicitly aligning visual tokens with textual concepts through a concept-level image-text contrastive loss is vital for achieving semantic independence and completeness, improving downstream task performance.
- The Token Merger, with its inner-cluster and inter-cluster Transformers and positional encoding, is essential for retaining fine-grained information and accurately modeling coherent scene semantics.
Summary by alphaXiv.
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