The architecture of deep convolutional networks (CNNs) has evolved for years, becoming more accurate and faster. However, it is still challenging to design reasonable network structures that aim at obtaining the best accuracy under a limited computational budget. In this paper, we propose a Tree block, named after its appearance, which extends the One-Shot Aggregation (OSA) module while being more lightweight and flexible. Specifically, the Tree block replaces each of the $3\times3$ Conv layers in OSA into a stack of shallow residual block (SRB) and $1\times1$ Conv layer. The $1\times1$ Conv layer is responsible for dimension increasing and the SRB is fed into the next step. By doing this, when aggregating the same number of subsequent feature maps, the Tree block has a deeper network structure while having less model complexity. In addition, residual connection and efficient channel attention(ECA) is added to the Tree block to further improve the performance of the network. Based on the Tree block, we build efficient backbone models calling TreeNets. TreeNet has a similar network architecture to ResNet, making it flexible to replace ResNet in various computer vision frameworks. We comprehensively evaluate TreeNet on common-used benchmarks, including ImageNet-1k for classification, MS COCO for object detection, and instance segmentation. Experimental results demonstrate that TreeNet is more efficient and performs favorably against the current state-of-the-art backbone methods.
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
Beijing Institute of Technology（北京理工大学）；University of Delaware（特拉华大学）；The Aviation Industry Corporation of China, Ltd.（中国航空工业集团有限公司）；Luoyang Institute of Electro-Optical Equipment（洛阳电光设备研究所）
Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院电子工程系）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Zhengzhou University（郑州大学）；Engineering Research Center of Intelligent Swarm Systems, Ministry of Education（教育部智能集群系统工程研究中心）；National Supercomputing Center in Zhengzhou（国家超级计算郑州中心）；Tianjin University（天津大学）；The University of Sydney（悉尼大学）
MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation
AI Research Lab, Department of Computer Science（人工智能研究实验室、计算机科学系）；Biomedical & Translational Sciences, Sanford School of Medicine（生物医学与转化科学、桑福德医学院）；University Of South Dakota（南达科他大学）
MAAM: A Lightweight Multi-Agent Aggregation Module for Efficient Image Classification Based on the MindSpore Framework
School of Computing and Information（计算与信息学院）；Network Security Research Center（网络安全研究中心）；Big Data and Policing Technology Laboratory（大数据与警务技术实验室）；Guangxi Police College（广西警察学院）；Chinese Academy of Sciences（中国科学院）；Institute of Software（软件研究所）
本文提出了一种基于长短期记忆（LSTM）的自适应聚合递归多视图立体视觉网络，即AA-RMVSNet。我们首先引入了一个视图内聚合模块，通过使用上下文感知卷积和多尺度聚合自适应地提取图像特征，有效地提高了具有挑战性的区域，如薄的物体和大的低纹理表面的性能。为了克服复杂场景中变化遮挡的困难，我们提出了一个视图间成本体积聚合模块，用于自适应像素视图聚合，该模块能够在所有视图之间保持更好的匹配对。提出的两个自适应聚合模块是轻量级的，有效的和互补的提高三维重建的准确性和完整性。代替传统的3D CNN，我们利用具有递归结构的混合网络进行成本体积正则化，这允许高分辨率重建和更精细的假设平面扫描。所提出的网络是端到端训练的，在各种数据集上都取得了优异的性能。该算法在Tanks and Temples基准测试中对$1^{st}$进行了排序，并在DTU数据集上取得了较好的结果，具有较强的泛化能力和鲁棒性。我们的方法的实现可在https://github.com/QT-Zhu/AA-RMVSNet上获得。
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
Segregation and Context Aggregation Network for Real-time Cloud Segmentation
Carnegie Mellon University（卡内基梅隆大学）；University of Nottingham（诺丁汉大学）；The University of Hong Kong（香港大学）；Cornell University（康奈尔大学）；University College Dublin（都柏林大学）
EPANet: Efficient Path Aggregation Network for Underwater Fish Detection
Dalian Key Laboratory of Smart Fishery（大连智能渔业重点实验室）；College of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；Key Laboratory of Environment Controlled Aquaculture（环境可控水产养殖重点实验室）；Dalian Polytechnic University School of International Education（大连职业技术学院国际教育学院）
PE-Mamba: Bidirectional Selective Layer Aggregation for AI-Generated Image Detection
College of Innovation & Technology, University of Michigan-Flint（密歇根大学弗林特分校创新与技术学院）；School of Electronics and Information Engineering, Korea Aerospace University（韩国航空大学电子与信息工程学院）
Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
Nanjing University of Posts and Telecommunications（南京邮电大学）；Soochow University（苏州大学）；Shanghai University（上海大学）；Nanjing University of Science and Technology（南京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
