An Efficient RGB-D Indoor Scene-Parsing Solution via Lightweight Multiflow Intersection and Knowledge Distillation

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-03-13 DOI:10.1109/JSTSP.2024.3400030
Wujie Zhou;Yuming Zhang;Weiqing Yan;Lv Ye
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Abstract

The rapid progression of convolutional neural networks (CNNs) has significantly improved indoor scene parsing, transforming the fields of robotics, autonomous navigation, augmented reality, and surveillance. Currently, societal demand is propelling these technologies toward integration into mobile smart device applications. However, the processing capabilities of mobile devices cannot support the comprehensive system requirements of CNNs, which poses a challenge for several deep-learning applications. One promising solution to this predicament is the deployment of lightweight student networks. These streamlined networks learn from their robust, cloud-based counterparts—that is, teacher networks—through knowledge distillation (KD). This facilitates a reduction in parameter count and optimizes student classification. Furthermore, a lightweight multiflow intersection network (LMINet) is proposed and developed for red–green–blue–depth (RGB-D) indoor scene parsing. The proposed method relies on dual-frequency KD (FKD) and compression KD (CKD) methods. A multiflow intersection module is introduced to efficiently integrate feature information from disparate layers. To maximize the performance of lightweight LMINet student (LMINet-S) networks, the FKD module employs a discrete cosine transform to capture feature information from different frequencies, whereas the CKD module compresses the features of diverse layers and distills their corresponding dimensions. Experiments using the NYUDv2 and SUN-RGBD datasets demonstrate that our LMINet teacher (LMINet-T) model, LMINet-S (without KD), and LMINet-S* (LMINet-S with KD) outperform state-of-the-art scene-parsing tools without increasing the parameter count (26.2M). Consequently, the technology is now closer to integration into mobile devices.
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通过轻量级多流交叉和知识蒸馏实现高效 RGB-D 室内场景解析解决方案
卷积神经网络(CNN)的飞速发展极大地改进了室内场景解析,改变了机器人、自主导航、增强现实和监控领域。目前,社会需求正推动这些技术集成到移动智能设备应用中。然而,移动设备的处理能力无法支持 CNN 的全面系统要求,这给一些深度学习应用带来了挑战。解决这一困境的一个可行方案是部署轻量级学生网络。这些精简的网络通过知识提炼(KD)从其基于云的强大同类网络(即教师网络)中学习。这有助于减少参数数量,优化学生分类。此外,针对红-绿-蓝-深(RGB-D)室内场景解析,提出并开发了一种轻量级多流交叉网络(LMINet)。所提出的方法依赖于双频 KD(FKD)和压缩 KD(CKD)方法。该方法引入了多流交叉模块,以有效整合来自不同层的特征信息。为了最大限度地提高轻量级 LMINet 学生(LMINet-S)网络的性能,FKD 模块采用离散余弦变换来捕捉不同频率的特征信息,而 CKD 模块则压缩不同层的特征并提炼其相应维度。使用 NYUDv2 和 SUN-RGBD 数据集进行的实验表明,我们的 LMINet 教师(LMINet-T)模型、LMINet-S(不含 KD)和 LMINet-S*(含 KD 的 LMINet-S)在不增加参数数(26.2M)的情况下,性能优于最先进的场景解析工具。因此,该技术现在更接近于集成到移动设备中。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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