@inproceedings{
sirejiding2024taskinteractionfree,
title={Task-Interaction-Free Multi-Task Learning with Efficient Hierarchical Feature Representation},
author={Shalayiding Sirejiding and Bayram Bayramli and Yuxiang Lu and Yuwen Yang and Tamam Alsarhan and Hongtao Lu and Yue Ding},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=XuEviauUhY}
}
Traditional multi-task learning often relies on explicit  task interaction mechanisms to enhance multi-task performance. However, these approaches encounter challenges such as negative transfer when jointly learning multiple weakly correlated tasks. Additionally, these methods  handle encoded features at a large scale, which escalates computational complexity to ensure dense prediction task performance. In this study, we introduce a Task-Interaction-Free Network (TIF) for multi-task learning, which diverges from explicitly designed task interaction mechanisms. Firstly, we present a Scale Attentive-Feature Fusion Module (SAFF) to enhance each scale in the shared encoder to have rich task-agnostic encoded features. Subsequently, our proposed task and scale-specific decoders efficiently decode the enhanced features shared across tasks without necessitating task-interaction modules. Concretely, we utilize a Self-Feature Distillation Module (SFD) to explore task-specific features at lower scales and the Low-To-High Scale Feature Diffusion Module (LTHD) to diffuse global pixel relationships from low-level to high-level scales. Experiments on publicly available multi-task learning datasets validate that our TIF attains state-of-the-art performance.
Task-Interaction-Free Multi-Task Learning with Efficient Hierarchical Feature Representation
Shalayiding Sirejiding Bayram Bayramli Yuxiang Lu Yuwen Yang Tamam Alsarhan Hongtao Lu Yue Ding