Cross-Task Affinity Learning for Multitask Dense Scene Predictions

• URL: http://arxiv.org/abs/2401.11124v2
• Date: Wed, 06 Nov 2024 11:40:50 GMT
• Title: Cross-Task Affinity Learning for Multitask Dense Scene Predictions
• Authors: Dimitrios Sinodinos, Narges Armanfard,
• Abstract summary: Multitask learning (MTL) has become prominent for its ability to predict multiple tasks jointly. We introduce the Cross-Task Affinity Learning (CTAL) module, a lightweight framework that enhances task refinement in multitask networks. Our results demonstrate state-of-the-art MTL performance for both CNN and transformer backbones, using significantly fewer parameters than single-task learning.
• Score: 5.939164722752263
• License:
• Abstract: Multitask learning (MTL) has become prominent for its ability to predict multiple tasks jointly, achieving better per-task performance with fewer parameters than single-task learning. Recently, decoder-focused architectures have significantly improved multitask performance by refining task predictions using features from related tasks. However, most refinement methods struggle to efficiently capture both local and long-range dependencies between task-specific representations and cross-task patterns. In this paper, we introduce the Cross-Task Affinity Learning (CTAL) module, a lightweight framework that enhances task refinement in multitask networks. CTAL effectively captures local and long-range cross-task interactions by optimizing task affinity matrices for parameter-efficient grouped convolutions without concern for information loss. Our results demonstrate state-of-the-art MTL performance for both CNN and transformer backbones, using significantly fewer parameters than single-task learning. Our code is publicly available at https://github.com/Armanfard-Lab/EMA-Net.

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