Vision as Unified Multimodal Generation

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The SenseNova-Vision model formulates computer vision as unified multimodal generation, enabling a single model to handle diverse visual tasks without specialized architectures. Built upon an off-the-shelf Bagel-7B-MoT Unified Multimodal Model (UMM), SenseNova-Vision is trained primarily on the SenseNova-Vision Corpus, a 50-million-example instruction-response dataset. This corpus converts heterogeneous computer vision annotations into native text, image, and mixed text-and-image generation targets. The model achieves competitive performance across structured visual understanding, dense geometric prediction, segmentation, and multi-view visual geometry, matching leading task-specialized systems. It supports natural-language instructions and visual prompts, generating responses as text, images, or mixed outputs. Training involved a mixed-task fine-tuning strategy, high-resolution inputs up to 980 pixels, and 50K steps with 500 warm-up steps and a 0.995 EMA ratio. SenseNova-Vision also preserves core multimodal abilities, scoring 79.0 on MMVP (compared to Bagel's 83.3) and 0.85 on GenEval (compared to Bagel's 0.82). The model and corpus are publicly available.

Key takeaway

For AI Architects evaluating foundation models for computer vision, SenseNova-Vision demonstrates that a single unified multimodal model can achieve competitive performance across diverse tasks. You should consider this approach to consolidate specialized systems, reducing architectural complexity and improving supervision sharing. Explore the publicly available model and corpus to integrate its generative capabilities into your general-purpose foundation models, fostering programmable perception.

Key insights

Computer vision tasks can be unified into a single multimodal generation framework using text and image outputs.

Principles

• Heterogeneous CV annotations convert to instruction-response examples.
• Unified multimodal generation eliminates task-specific prediction heads.
• Joint training across modalities improves generalization to new task variants.

Method

Convert diverse computer vision annotations into instruction-response examples with text, image, or mixed targets, then fine-tune a pretrained UMM using a mixed-task joint sampling strategy.

In practice

• Use normalized image coordinates for structured visual understanding tasks.
• Represent dense geometric targets as encoded grayscale or RGB images.
• Employ mixed text-image responses for complex segmentation tasks.

Topics

• Unified Multimodal Models
• Computer Vision Tasks
• SenseNova-Vision Corpus
• Generative AI
• Dense Geometric Prediction
• Segmentation

Code references

• OpenSenseNova/SenseNova-Vision

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.