HirMTL: Hierarchical Multi-Task Learning for dense scene understanding

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-11-04 DOI:10.1016/j.neunet.2024.106854
Huilan Luo , Weixia Hu , Yixiao Wei , Jianlong He , Minghao Yu
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Abstract

In the realm of artificial intelligence, simultaneous multi-task learning is crucial, particularly for dense scene understanding. To address this, we introduce HirMTL, a novel hierarchical multi-task learning framework designed to enhance dense scene analysis. HirMTL is adept at facilitating interaction at the scale level, ensuring task-adaptive multi-scale feature fusion, and fostering task-level feature interchange. It leverages the inherent correlations between tasks to create a synergistic learning environment. Initially, HirMTL enables concurrent sharing and fine-tuning of features at the single-scale level. This is further extended by the Task-Adaptive Fusion module (TAF), which intelligently blends features across scales, specifically attuned to each task’s unique requirements. Complementing this, the Asymmetric Information Comparison Module (AICM) skillfully differentiates and processes both shared and unique features, significantly refining task-specific performance with enhanced accuracy. Our extensive experiments on various dense prediction tasks validate HirMTL’s exceptional capabilities, showcasing its superiority over existing multi-task learning models and underscoring the benefits of its hierarchical approach.
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HirMTL:用于密集场景理解的分层多任务学习。
在人工智能领域,同步多任务学习至关重要,尤其是在理解密集场景时。为此,我们推出了 HirMTL,这是一种新颖的分层多任务学习框架,旨在加强密集场景分析。HirMTL 擅长促进尺度级别的交互,确保任务自适应的多尺度特征融合,并促进任务级别的特征交换。它利用任务之间固有的相关性创造了一个协同学习环境。最初,HirMTL 可在单尺度级别实现特征的并发共享和微调。任务自适应融合模块(TAF)进一步扩展了这一功能,它能智能地融合不同尺度的特征,特别适合每个任务的独特要求。作为补充,非对称信息比较模块(AICM)能巧妙地区分和处理共享特征和独特特征,从而显著提高特定任务的性能和准确性。我们在各种密集预测任务上进行了大量实验,验证了 HirMTL 的卓越能力,展示了它优于现有多任务学习模型的优势,并强调了其分层方法的好处。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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