Multi-task dense prediction improves pixel-level performance by leveraging shared representations and inter-task collaboration. However, existing approaches either rely on implicit task relationships or neglect frequency-domain cues that are essential for preserving fine-grained details and enhancing cross-task feature learning at multiple scales. As a result, they face persistent challenges in multi-scale feature fusion, effective task interaction, and accurate decoding. To address these issues, we propose a hierarchical frequency-driven framework, termed Hierarchical Frequency-Adaptive Network (HiFAN), that facilitates cross-task collaborative optimization via frequency-domain analysis. Specifically, we first design a task-adaptive fusion module that exploits multi-scale frequency-domain information to enhance spatial details. This module generates dynamic convolutional kernels with task-specific parameters and positional biases to adaptively accommodate diverse task requirements. Next, we introduce an efficient cross-task interaction module that leverages compact low-frequency representations to enable global context exchange across tasks. Finally, we present a high-frequency-aware decoder that mitigates feature smoothing and detail loss commonly introduced by Transformer-based decoders. We demonstrate the effectiveness of HiFAN on two standard multi-task learning benchmarks, PASCAL-Context and NYUD-v2, achieving strong and competitive performance across multiple tasks. The code and model weights are available in HiFAN.
Zhuge et al. (Thu,) studied this question.