arXiv Computer Vision
Patch-Derived or Register Tokens? Comparing Variable-Length Image Tokenizers
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arXiv Computer Vision
Oct 8
QuadTok: Quadtree Visual Tokenizer for Autoregressive Image Generation
QuadTok introduces a quadtree-based visual tokenizer that replaces traditional 2D grids or 1D token sequences with a hierarchical structure, allowing dynamic allocation of tokens to visually complex regions while using coarser resolution for homogeneous areas. The tokenizer, trained on ImageNet, reduces token usage by about 10% compared to a fixed 256-token grid and retains comparable reconstruction fidelity. Leveraging the tree’s natural causality, a 947 M GPT-style model conditioned on a quadtree topology achieves a 2.08 gFID on ImageNet 256×256 and supports zero‑shot spatially controlled image generation.
arXiv Machine Learning
Oct 12
Decoding Register Tokens Preserves Global Scene Details
The paper investigates whether keeping non‑spatial register tokens in vision transformer decoders improves image reconstruction and generation. Using representation autoencoders with frozen register‑equipped encoders, the authors show that joint self‑attention over both patch and register tokens yields better reconstruction quality on ImageNet‑1K, with lower FID scores and higher PSNRs compared to patch‑only baselines. For generation, a multi‑head attention pooling predictor recovers register features from sampled patches, further reducing FID by nearly 10% over the patch‑only approach.
arXiv Computer Vision
Sep 3
ZipTok3D: High-Fidelity 3D Tokenization with Compact Token Prefixes
ZipTok3D is a 3D tokenizer that achieves high‑fidelity reconstruction from extremely short token sequences by organizing object geometry into progressively informative global‑token prefixes. During training, nested dropout truncates the latent sequence, forcing each retained prefix to reconstruct the full object, which prioritizes essential geometric information in the leading tokens. The decoder uses a parameter‑shared Transformer block to iteratively recover fine‑grained geometry, enabling reconstruction quality comparable to a 32‑token baseline while using only one token on ShapeNet and four on TRELLIS.
arXiv AI
Oct 5
VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation
VTBench is a new benchmark that evaluates visual tokenizers (VTs) used in autoregressive image generation. It assesses VTs on image reconstruction, detail preservation, and text preservation across diverse scenarios, revealing that continuous VAEs outperform discrete VTs in maintaining spatial structure and semantic detail. The study also explores GPT‑4o’s potential autoregressive behavior and releases the benchmark publicly to encourage development of robust, open‑source VTs.
arXiv AI
Jun 2
Channel-wise Vector Quantization
arXiv:2605. 26089v2 Announce Type: replace-cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens.
arXiv Computer Vision
Aug 26
Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation
arXiv:2608.24293v1 Announce Type: new Abstract: Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with varia...