Harmonizing Visual Representations for Unified Multimodal Understanding and Generation

• URL: http://arxiv.org/abs/2503.21979v2
• Date: Tue, 22 Apr 2025 04:26:58 GMT
• Title: Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
• Authors: Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin, Zhonghua Wu, Qingyi Tao, Wentao Liu, Wei Li, Chen Change Loy,
• Abstract summary: We present emphHarmon, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder.<n>Harmon achieves state-of-the-art image generation results on the GenEval, MJHQ30K and WISE benchmarks.
• Score: 53.01486796503091
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity. Current approaches that utilize vector quantization (VQ) or variational autoencoders (VAE) for unified visual representation prioritize intrinsic imagery features over semantics, compromising understanding performance. In this work, we take inspiration from masked image modelling (MIM) that learns rich semantics via a mask-and-reconstruct pre-training and its successful extension to masked autoregressive (MAR) image generation. A preliminary study on the MAR encoder's representation reveals exceptional linear probing accuracy and precise feature response to visual concepts, which indicates MAR's potential for visual understanding tasks beyond its original generation role. Based on these insights, we present \emph{Harmon}, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder. Through a three-stage training procedure that progressively optimizes understanding and generation capabilities, Harmon achieves state-of-the-art image generation results on the GenEval, MJHQ30K and WISE benchmarks while matching the performance of methods with dedicated semantic encoders (e.g., Janus) on image understanding benchmarks. Our code and models will be available at https://github.com/wusize/Harmon.

Related papers

• UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding [84.87802580670579]
  We introduce UniToken, an auto-regressive generation model that encodes visual inputs through a combination of discrete and continuous representations. Our unified visual encoding framework captures both high-level semantics and low-level details, delivering multidimensional information.
  arXiv  Detail & Related papers  (2025-04-06T09:20:49Z)
• ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement [68.05833403672274]
  Existing unified models have struggled to handle the three fundamental capabilities in a unified model: understanding, generation, and editing. ILLUME+ introduces a unified dual visual tokenizer, DualViTok, which preserves fine-grained textures and text-aligned semantics. We also employ a diffusion model as the image detokenizer for enhanced generation quality and efficient super-resolution.
  arXiv  Detail & Related papers  (2025-04-02T17:45:00Z)
• MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization [43.12251414524675]
  Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great success in both self-supervised pre-training and image generation. We propose MergeVQ, which incorporates token merging techniques into VQ-based generative models to bridge the gap between image generation and visual representation learning.
  arXiv  Detail & Related papers  (2025-04-01T17:39:19Z)
• Unified Autoregressive Visual Generation and Understanding with Continuous Tokens [52.21981295470491]
  We present UniFluid, a unified autoregressive framework for joint visual generation and understanding.<n>Our unified autoregressive architecture processes multimodal image and text inputs, generating discrete tokens for text and continuous tokens for image.<n>We find though there is an inherent trade-off between the image generation and understanding task, a carefully tuned training recipe enables them to improve each other.
  arXiv  Detail & Related papers  (2025-03-17T17:58:30Z)
• SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation [73.98487014058286]
  SemHiTok is a unified image tokenizer via Semantic-Guided Hierarchical codebook.<n>We show that SemHiTok achieves excellent rFID score at 256X256resolution compared to other unified tokenizers.
  arXiv  Detail & Related papers  (2025-03-09T20:42:34Z)
• VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model [38.61292051733335]
  We present VARGPT, a novel multimodal large language model (MLLM) that unifies visual understanding and generation within a single autoregressive framework.<n>VarGPT employs a next-token prediction paradigm for visual understanding and a next-scale prediction paradigm for visual autoregressive generation.<n> Notably, VARGPT naturally supports capabilities in autoregressive visual generation and instruction-to-image synthesis, showcasing its versatility in both visual understanding and generation tasks.
  arXiv  Detail & Related papers  (2025-01-21T17:50:43Z)
• Gaussian Masked Autoencoders [74.2341070024126]
  This paper explores Masked Autoencoders (MAE) with Gaussian Splatting.<n>Our approach, named Gaussian Masked Autoencoder, or GMAE, aims to learn semantic abstractions and spatial understanding jointly.
  arXiv  Detail & Related papers  (2025-01-06T18:59:57Z)
• Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic Forgetting [49.87694319431288]
  Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources. We propose a Comprehensive Generative (CGR) framework that restores appearance and semantic knowledge by synthesizing image-mask pairs. Experiments on incremental tasks (cardiac, fundus and prostate segmentation) show its clear advantage for alleviating concurrent appearance and semantic forgetting.
  arXiv  Detail & Related papers  (2024-06-28T10:05:58Z)
• MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments [72.6405488990753]
  Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks. We propose a single-stage and standalone method, MOCA, which unifies both desired properties. We achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols.
  arXiv  Detail & Related papers  (2023-07-18T15:46:20Z)
• MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis [33.46831766206675]
  MAsked Generative (MAGE) is first framework to unify SOTA image generation and self-supervised representation learning. Inspired by previous generative models, MAGE uses semantic tokens learned by a vector-quantized GAN at inputs and outputs. On ImageNet-1K, a single MAGE ViT-L model obtains 9.10 FID in the task of class-unconditional image generation.
  arXiv  Detail & Related papers  (2022-11-16T18:59:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.