[Paper] VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction

Source: arXiv - 2511.23386v1

Overview

The paper introduces VQRAE, a novel auto‑encoder that bridges the gap between visual understanding, generation, and reconstruction by using a single tokenizer. By marrying continuous semantic embeddings with discrete visual tokens, VQRAE promises a unified front‑end for multimodal models—something that has traditionally required separate pipelines.

Key Contributions

• Unified tokenizer that simultaneously yields:
  1. High‑dimensional continuous features for downstream understanding tasks (e.g., classification, detection).
  2. Low‑dimensional discrete tokens suitable for autoregressive generation and fine‑grained reconstruction.
• Two‑stage training recipe:
  • Stage 1 – Freeze a pretrained Vision Transformer (ViT) encoder and learn a high‑capacity vector‑quantized (VQ) codebook via pixel‑level reconstruction.
  • Stage 2 – Jointly fine‑tune the encoder with self‑distillation, preserving semantic richness while aligning to the discrete codebook.
• High‑dimensional VQ codebook (1536‑D) that achieves 100 % utilization, overturning the conventional wisdom that VQ for images must be low‑dimensional.
• Empirical validation across three fronts—visual understanding, image generation, and reconstruction—showing competitive results and strong scaling behavior in autoregressive settings.

Methodology

1. Backbone – The encoder is a pretrained ViT (e.g., ViT‑B/16) that already captures rich semantic information from images.
2. Symmetric ViT Decoder – Mirrors the encoder architecture, enabling pixel‑level reconstruction from latent codes.
3. Vector Quantization Layer – A learnable codebook of 1536‑dimensional vectors. During forward passes, encoder outputs are snapped to the nearest codebook entry, producing discrete tokens.
4. Training Pipeline
   • Stage 1 (Codebook pre‑training):
     • Encoder weights are frozen.
     • The decoder learns to reconstruct the original image from quantized tokens, driving the codebook to cover the visual space.
   • Stage 2 (Joint fine‑tuning):
     • Encoder is unfrozen and optimized with a self‑distillation loss that forces its continuous outputs to stay close to the quantized version, preserving semantic fidelity.
5. Losses – Pixel reconstruction (L2/LPIPS), commitment loss for VQ, and a distillation term that aligns continuous and discrete representations.

Results & Findings

The authors also report linear scaling of generation quality with model size in the autoregressive decoder, indicating that the discrete token space remains expressive as we grow the model.

Practical Implications

• Single‑tokenizer pipelines: Developers can now feed the same visual token stream into both a classifier and a generative model, simplifying data handling and reducing engineering overhead.
• Better token efficiency: High‑dimensional VQ reduces the number of tokens needed for high‑fidelity reconstruction, which translates to lower memory usage and faster inference for transformer‑based generators.
• Plug‑and‑play with existing foundations: Since VQRAE builds on off‑the‑shelf pretrained ViTs, teams can retrofit the tokenizer onto current vision backbones without retraining from scratch.
• Cross‑modal research: The unified representation opens doors for multimodal tasks (e.g., image‑captioning, visual question answering) where the same token set can be consumed by language models, enabling tighter vision‑language integration.
• Scalable generation: Autoregressive decoders that operate on VQRAE tokens inherit the benefits of discrete modeling (exact likelihood, controllable sampling) while retaining semantic richness, useful for content creation tools, game asset pipelines, and synthetic data generation.

Limitations & Future Work

• Training cost – The two‑stage procedure, especially the high‑dimensional codebook learning, demands substantial GPU hours and large batches.
• Token length – Although the codebook is high‑dimensional, the number of tokens per image remains comparable to other VQ‑based models, which can be a bottleneck for very high‑resolution inputs.
• Generalization to non‑visual modalities – The current design focuses on images; extending the approach to video or 3‑D data may require architectural tweaks.
• Future directions suggested by the authors include: exploring hierarchical codebooks for multi‑scale generation, integrating the tokenizer directly into multimodal transformer architectures (e.g., CLIP‑style models), and reducing the computational footprint through distillation or quantization‑aware training.

Authors

• Sinan Du
• Jiahao Guo
• Bo Li
• Shuhao Cui
• Zhengzhuo Xu
• Yifu Luo
• Yongxian Wei
• Kun Gai
• Xinggang Wang
• Kai Wu
• Chun Yuan

Paper Information

• arXiv ID: 2511.23386v1
• Categories: cs.CV
• Published: November 28, 2025
• PDF: Download PDF