Visual Bridge: Universal Visual Perception Representations Generating

• URL: http://arxiv.org/abs/2511.07877v1
• Date: Wed, 12 Nov 2025 01:26:00 GMT
• Title: Visual Bridge: Universal Visual Perception Representations Generating
• Authors: Yilin Gao, Shuguang Dou, Junzhou Li, Zhiheng Yu, Yin Li, Dongsheng Jiang, Shugong Xu,
• Abstract summary: We propose a universal visual perception framework based on flow matching that can generate diverse visual representations across multiple tasks.<n>Our approach formulates the process as a universal flow-matching problem from image patch tokens to task-specific representations.<n>Our model achieves competitive performance in both zero-shot and fine-tuned settings, outperforming prior generalist and several specialist models.
• Score: 27.034175361589572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However, these models are often restricted by a ``single-task-single-model'' paradigm, severely limiting their generalizability and scalability in multi-task scenarios. Motivated by the cross-domain generalization ability of large language models, we propose a universal visual perception framework based on flow matching that can generate diverse visual representations across multiple tasks. Our approach formulates the process as a universal flow-matching problem from image patch tokens to task-specific representations rather than an independent generation or regression problem. By leveraging a strong self-supervised foundation model as the anchor and introducing a multi-scale, circular task embedding mechanism, our method learns a universal velocity field to bridge the gap between heterogeneous tasks, supporting efficient and flexible representation transfer. Extensive experiments on classification, detection, segmentation, depth estimation, and image-text retrieval demonstrate that our model achieves competitive performance in both zero-shot and fine-tuned settings, outperforming prior generalist and several specialist models. Ablation studies further validate the robustness, scalability, and generalization of our framework. Our work marks a significant step towards general-purpose visual perception, providing a solid foundation for future research in universal vision modeling.

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