4 Conclusion

本文为论文《Janus-Pro：统一多模态理解与生成》「论文原文」板块的对应章节，按 arXiv 官方全文逐字保留，层级与「中文全文翻译」板块一一对应。

This paper introduces improvements to Janus from three aspects: training strategy, data, and model size. These enhancements have led to significant advancements in both multimodal understanding and text-to-image instruction-following capabilities. However, Janus-Pro still has certain limitations. In terms of multimodal understanding, the input resolution is limited to 384 $\times$ 384, which affects its performance in fine-grained tasks such as OCR. For text-to-image generation, the low resolution, combined with reconstruction losses introduced by the vision tokenizer, results in images that, while rich in semantic content, still lack fine details. For example, small facial regions occupying limited image space may appear under-detailed. Increasing the image resolution could mitigate these issues.