改进灵活的图像标记器以用于自回归图像生成
Improving Flexible Image Tokenizers for Autoregressive Image Generation
AI总结：
ReToK通过冗余标记填充和层次语义正则化改进灵活图像标记器，提升自回归图像生成效果。
摘要：
灵活的图像标记器旨在使用一个有序的1D可变长度标记序列来表示图像。这种灵活的标记化通常是通过嵌套丢弃实现的，其中在训练过程中随机截断一部分尾部标记，然后使用剩余的前面序列来重建图像。然而，这种尾部截断策略本质上将图像信息集中在早期标记中，限制了随着标记长度增加而下游自回归（AR）图像生成的效果。为克服这些限制，我们提出了ReToK，一种具有冗余标记填充和层次语义正则化的灵活标记器，旨在充分利用所有标记以提高潜在建模。具体来说，我们引入了冗余标记填充以更频繁地激活尾部标记，从而缓解早期标记中的信息过集中问题。此外，我们应用了层次语义正则化，以对齐早期标记的解码特征与预训练视觉基础模型的特征，同时逐步减少正则化强度以允许更精细的低级细节重建。广泛的实验表明ReToK的有效性：在ImageNet 256×256上，我们的方法在与灵活和固定长度标记器相比时实现了更优越的生成性能。代码将在：https://github.com/zfu006/ReTok上提供。
英文摘要：
Flexible image tokenizers aim to represent an image using an ordered 1D variable-length token sequence. This flexible tokenization is typically achieved through nested dropout, where a portion of trailing tokens is randomly truncated during training, and the image is reconstructed using the remaining preceding sequence. However, this tail-truncation strategy inherently concentrates the image information in the early tokens, limiting the effectiveness of downstream AutoRegressive (AR) image generation as the token length increases. To overcome these limitations, we propose \textbf{ReToK}, a flexible tokenizer with \underline{Re}dundant \underline{Tok}en Padding and Hierarchical Semantic Regularization, designed to fully exploit all tokens for enhanced latent modeling. Specifically, we introduce \textbf{Redundant Token Padding} to activate tail tokens more frequently, thereby alleviating information over-concentration in the early tokens. In addition, we apply \textbf{Hierarchical Semantic Regularization} to align the decoding features of earlier tokens with those from a pre-trained vision foundation model, while progressively reducing the regularization strength toward the tail to allow finer low-level detail reconstruction. Extensive experiments demonstrate the effectiveness of ReTok: on ImageNet 256$\times$256, our method achieves superior generation performance compared with both flexible and fixed-length tokenizers. Code will be available at: \href{https://github.com/zfu006/ReTok}{https://github.com/zfu006/ReTok}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
