苹果传统强项再发力，视觉领域三种模态终于统一

Core Insights - The article discusses the recent release of Apple's new products and the ongoing conversation about the hardware advancements of the new phones [1] - It highlights that Apple has not yet introduced any groundbreaking AI applications, with Apple Intelligence still lagging in the domestic market [2] - The article notes a concerning trend of talent loss within Apple's AI and hardware teams, suggesting a less optimistic outlook for the company [3] AI Research and Development - Despite challenges in the large model domain, Apple has a strong background in computer vision research [4] - The article emphasizes a significant pain point in building large models related to vision, as visual modalities (images, videos, and 3D) require separate handling due to their different data dimensions and representation methods [4][5] - Apple’s research team has proposed ATOKEN, a unified tokenizer for vision, which addresses the core limitation of existing models by enabling unified processing across all major visual modalities while maintaining reconstruction quality and semantic understanding [5][6][8] ATOKEN Architecture - ATOKEN represents a significant innovation by introducing a shared sparse 4D latent space that allows for the representation of all visual modalities as feature-coordinate pairs [11] - The architecture utilizes a pure Transformer framework, surpassing traditional convolutional methods, and incorporates a four-stage progressive training curriculum to enhance multimodal learning without degrading single modality performance [15][16][19] - The training phases include image-based pre-training, video dynamic modeling, integration of 3D geometry, and discrete tokenization through finite scalar quantization [19][20] Performance Metrics - ATOKEN demonstrates industry-leading performance across various evaluation metrics, achieving high-quality image reconstruction and semantic understanding [21][23] - In image tokenization, ATOKEN achieved a reconstruction performance of 0.21 rFID at a 16×16 compression on ImageNet, outperforming the UniTok method [23] - For video processing, it achieved 3.01 rFVD and 33.11 PSNR on the DAVIS dataset, indicating competitive performance with specialized video models [24] - In 3D asset handling, ATOKEN achieved 28.28 PSNR on the Toys4k dataset, surpassing dedicated 3D tokenizers [29] Conclusion - The results indicate that the next generation of multimodal AI systems based on unified visual tokenization is becoming a reality, showcasing ATOKEN's capabilities in both generative and understanding tasks [26][27]