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

• URL: http://arxiv.org/abs/2506.20279v1
• Date: Wed, 25 Jun 2025 09:40:50 GMT
• Title: From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios
• Authors: Changliang Xia, Chengyou Jia, Zhuohang Dang, Minnan Luo,
• Abstract summary: We propose DenseDiT, which exploits generative models' visual priors to perform diverse real-world dense prediction tasks.<n>DenseDiT achieves superior results using less than 0.01% training data of baselines, underscoring its practical value for real-world deployment.
• Score: 12.06521067086988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated label for an input image. Despite advances in this field, existing methods primarily focus on idealized conditions, with limited generalization to real-world scenarios and facing the challenging scarcity of real-world data. 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. Then, we propose DenseDiT, which maximally 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, working with less than 0.1% additional parameters. 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. Our data, and checkpoints and codes are available at https://xcltql666.github.io/DenseDiTProj

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