From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios

Changliang Xia; Chengyou Jia; Zhuohang Dang; Minnan Luo; Zhihui Li; Xiaojun Chang

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arXiv · 2506.20279

From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios

Changliang Xia·Chengyou Jia·Zhuohang Dang·Minnan Luo·Zhihui Li·Xiaojun Chang

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Abstract

Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated labels for input images. Despite advances in this field, existing methods primarily focus on idealized conditions, exhibiting limited real-world generalization and struggling with the acute scarcity of real-world data in practical scenarios. To systematically study this problem, we first introduce DenseWorld, a benchmark spanning a broad set of 25 dense prediction tasks that correspond to urgent real-world applications, featuring unified evaluation across tasks. We then propose DenseDiT, which exploits generative models' visual priors to perform diverse real-world dense prediction tasks through a unified strategy. DenseDiT combines a parameter-reuse mechanism and two lightweight branches that adaptively integrate multi-scale context. This design enables DenseDiT to achieve efficient tuning with less than 0.1% additional parameters, activating the visual priors while effectively adapting to diverse real-world dense prediction tasks. Evaluations on DenseWorld reveal significant performance drops in existing general and specialized baselines, highlighting their limited real-world generalization. In contrast, DenseDiT achieves superior results using less than 0.01% training data of baselines, underscoring its practical value for real-world deployment.

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Changliang Xia, Chengyou Jia, Zhuohang Dang, Minnan Luo, Zhihui Li, Xiaojun Chang. 2025-09-30. From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios. https://arxiv.org/abs/2506.20279

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