Jodi: Unification of Visual Generation and Understanding via Joint Modeling

• URL: http://arxiv.org/abs/2505.19084v1
• Date: Sun, 25 May 2025 10:40:52 GMT
• Title: Jodi: Unification of Visual Generation and Understanding via Joint Modeling
• Authors: Yifeng Xu, Zhenliang He, Meina Kan, Shiguang Shan, Xilin Chen,
• Abstract summary: We propose Jodi, a diffusion framework that unifies visual generation and understanding.<n>Jodi is built upon a linear diffusion transformer along with a role switch mechanism.<n>We present the Joint-1.6M dataset, which contains 200,000 high-quality images.
• Score: 72.2478082170191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual generation and understanding are two deeply interconnected aspects of human intelligence, yet they have been traditionally treated as separate tasks in machine learning. In this paper, we propose Jodi, a diffusion framework that unifies visual generation and understanding by jointly modeling the image domain and multiple label domains. Specifically, Jodi is built upon a linear diffusion transformer along with a role switch mechanism, which enables it to perform three particular types of tasks: (1) joint generation, where the model simultaneously generates images and multiple labels; (2) controllable generation, where images are generated conditioned on any combination of labels; and (3) image perception, where multiple labels can be predicted at once from a given image. Furthermore, we present the Joint-1.6M dataset, which contains 200,000 high-quality images collected from public sources, automatic labels for 7 visual domains, and LLM-generated captions. Extensive experiments demonstrate that Jodi excels in both generation and understanding tasks and exhibits strong extensibility to a wider range of visual domains. Code is available at https://github.com/VIPL-GENUN/Jodi.

Related papers

• ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation [108.69315278353932]
  We introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images.<n>By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.
  arXiv  Detail & Related papers  (2025-02-25T16:57:04Z)
• Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation [44.457347230146404]
  We leverage the scene graph, a powerful structured representation, for complex image generation. We employ the generative capabilities of variational autoencoders and diffusion models in a generalizable manner. Our method outperforms recent competitors based on text, layout, or scene graph.
  arXiv  Detail & Related papers  (2024-10-01T07:02:46Z)
• From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation [19.096741614175524]
  Parts2Whole is a novel framework designed for generating customized portraits from multiple reference images. We first develop a semantic-aware appearance encoder to retain details of different human parts. Second, our framework supports multi-image conditioned generation through a shared self-attention mechanism.
  arXiv  Detail & Related papers  (2024-04-23T17:56:08Z)
• Instruct-Imagen: Image Generation with Multi-modal Instruction [90.04481955523514]
  instruct-imagen is a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain.
  arXiv  Detail & Related papers  (2024-01-03T19:31:58Z)
• T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up [16.165889084870116]
  We present an end-to-end approach to generate high-resolution person images conditioned on texts only. We develop an effective generative model to produce person images with two novel mechanisms.
  arXiv  Detail & Related papers  (2022-08-18T07:41:02Z)
• Local and Global GANs with Semantic-Aware Upsampling for Image Generation [201.39323496042527]
  We consider generating images using local context. We propose a class-specific generative network using semantic maps as guidance. Lastly, we propose a novel semantic-aware upsampling method.
  arXiv  Detail & Related papers  (2022-02-28T19:24:25Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering [100.32273175423146]
  We present a method for simultaneously learning, in an unsupervised manner, a conditional image generator, foreground extraction and segmentation, and object removal and background completion. The method combines a Geneversarative Adrial Network and a Variational Auto-Encoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once.
  arXiv  Detail & Related papers  (2019-12-31T18:15:58Z)

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.