Image Tokenization

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters
May 6, 2026Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan +9Image Tokenization Autoencoders
University of Texas, Austin · University of Washington · 3Stanford University +3
End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
May 1, 2026Wenda Chu, Bingliang Zhang, Jiaqi Han +4Image Tokenization Visual Tokenization
ByteDance Seed · California Institute of Technology · Stanford University
VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations
Apr 27, 2026Maitreya Patel, Jingtao Li, Weiming Zhuang +2Image Tokenization Visual Tokenization
Arizona State University · SonyAI · 3Work partially done while at SonyAI. Maitreya is now at Adobe.
(1D) Ordered Tokens Enable Efficient Test-Time Search
Apr 16, 2026Zhitong Gao, Parham Rezaei, Ali Cy +7Image Tokenization Test-Time Scaling
1Swiss Federal Institute of Technology Lausanne (EPFL) · 2Apple
COMiT: Learning Structured Visual Tokens through Sequential Communication
Feb 24, 2026Aram Davtyan, Yusuf Sahin, Yasaman Haghighi +4Image Tokenization Visual Tokenization
Computer Vision Group, University of Bern, Switzerland · VITA Lab, EPFL, Switzerland
LGQ: Learnable Geometric Quantization for Image Tokenization
Feb 17, 2026Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton +2Image Tokenization Vector Quantization
Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington · Department of Computer Science, University of Illinois Springfield · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Oct 6, 2025Théophane Vallaeys, Jakob Verbeek, Matthieu CordImage Tokenization Image Reconstruction
Meta Fundamental AI Research · Sorbonne University
Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs
Date pendingGuiqiu Liao, Matjaz Jogan, Daniel A. HashimotoImage Tokenization Healthcare
GRASP Laboratory, University of Pennsylvania · PCASO Laboratory, Department of Surgery, University of Pennsylvania · Department of Computer and Information Science, University of Pennsylvania

May 6, 2026, cs.CVJ/K move · Enter open · S save

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters

Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan, Orr Zohar+8

University of Texas, Austin · University of Washington · 3Stanford University+3

Image Tokenization Autoencoders Perceptual Loss Visual Tokenization Image Reconstruction Visual Token Compression

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Vision Transformer (ViT) autoencoders have emerged as compelling tokenizers for images, offering improved reconstruction over convolutional tokenizers. However, existing ViT tokenizers cannot explore this landscape as performance degrades outside training resolutions, and reliance on adversarial losses prevents stable scaling. ViTok (Hansen-Estruch et al., 2025) found that the compression ratio r mediates a reconstruction-generation trade-off where lower r means better reconstructions but harder generations, so improving tokenizer reconstruction is key to more Pareto-optimal tokenizers. We introduce ViTok-v2, which addresses these limitations with native resolution support via NaFlex for generalization across resolutions and aspect ratios, and a novel DINOv3 perceptual loss that replaces both LPIPS and GAN objectives for stable training at any scale. ViTok-v2 is trained on about 2B images and scaled to 5B parameters, the largest image autoencoder to date. ViTok-v2 matches or exceeds state-of-the-art reconstruction at 256p and outperforms all baselines at 512p and above. In joint scaling experiments with flow matching generators, we show that scaling both the autoencoder and the generator advances the Pareto frontier of this trade-off.

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Latest papers 38