Evolving task-agnostic features from task-specific priors for multi-task dense prediction

Publication Type

Journal Article

Publication Date

3-2026

Abstract

Previous multi-task dense prediction methods based on a decoder-centric paradigm have achieved promising results. They primarily rely on task-agnostic features extracted from a pre-trained backbone, overlooking the importance of task-specific prior knowledge. In this paper, we propose a novel approach that integrates task priors into task-agnostic features to facilitate the development of a more refined task-specific representativeness. Specifically, we introduce a CNN-based task-prior awareness module to capture comprehensive task-specific priors. As the cornerstone of feature interaction, task-agnostic features (e.g., pretrained ViT tokens) recurrently absorb the extracted task priors, evolving into multi-task-informed features. In the meantime, multi-task-informed features enhance task-specific features in favor of dense prediction. Furthermore, to address the inherent incompatibility between ViT tokens and CNN-based priors, we incorporate multi-scale collaborative feature filters within the decoder that adaptively generate low-pass and high-pass kernels to modulate their collaboration. Extensive experiments on the PASCAL-Context and NYUD-v2 datasets demonstrate that our approach achieves comparable or even better performance than the existing state-of-the-art methods.

Keywords

Dense Prediction, Multi-Task Learning

Discipline

Artificial Intelligence and Robotics | Software Engineering

Publication

IEEE Transactions on Multimedia

First Page

1

Last Page

13

ISSN

1520-9210

Identifier

10.1109/TMM.2026.3673564

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TMM.2026.3673564

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