舰船目标检测是遥感领域的一个重要课题，也是一个充满挑战的课题。应用场景的复杂性、检测区域的冗余性、密集舰船检测的困难性等都是限制传统方法在舰船检测中成功运行的主要障碍。针对上述问题，本文提出了一种全新的基于多任务旋转区域卷积神经网络的检测模型。该模型主要由五个部分组成：密集特征金字塔网络（Dense Feature Pyramid Network，DFPN）、自适应感兴趣区域（Adaptive Region of Interest，ROI）对齐、旋转限界框回归、船首方向预测和旋转非最大值抑制（Rotational Nonmaximum Suppression，R-NMS）。首先，通过多尺度特征网络，充分利用了低层的位置信息和高层的语义信息。然后，我们设计了自适应ROI对齐，以获得高质量的建议，保持完整的空间和语义信息。与大多数以前的方法不同，通过我们的方法获得的预测是具有较少冗余区域的对象的最小外接矩形。因此，旋转区域检测框架比传统的检测模型更适合检测密集目标。另外，通过预测还可以确定船舶的靠泊和航行方向。基于SRSS的旋转检测的详细评估表明，我们的检测方法具有竞争力的性能。
英文摘要：
Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multitask rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design Adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS for rotation detection shows that our detection method has a competitive performance.
Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images
School of Electronic and Information Engineering, Beihang University（北京航空航天大学电子与信息工程学院）；Edinburgh Napier University（爱丁堡龙比亚大学）；Taibah University（塔伊巴大学）；University of Strathclyde（斯特拉斯克莱德大学）；University of Leicester（莱斯特大学）
常用的水平限界框（HBB）方法不能准确定位合成孔径雷达（SAR）图像中任意方位的细长舰船目标。因此，近年来，基于有向限界框（OBB）的方法逐渐受到研究者的关注。然而，最近提出的大多数基于深度学习的OBB检测方法都会遇到角度或关键点回归中的边界不连续问题。为了缓解这个问题，研究人员提出引入一些手动设置的参数或额外的网络分支来区分边界情况，这使得训练更加困难，导致性能下降。为了解决OBB回归中的边界不连续问题，本文提出了通过学习极坐标编码来检测SAR舰船。该编码方案使用一组从目标中心指向边界点的矢量来表示OBB。直接根据极坐标编码进行训练和推理，避免了边界不连续问题。此外，我们提出了一个Intersect over Union（IOU）加权回归损失，通过IOU度量进一步指导极性编码的训练，提高检测性能。在旋转SAR舰船检测数据集（RSSDD）上的实验结果表明，该方法的检测性能优于其他算法和OBB编码方案，证明了该方法的有效性。
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光耀医学院）；Yunnan United Vision Technology Co., Ltd（云南联合视觉科技有限公司）；Department of Computer Science and Engineering, University at Buffalo, State University of New York（纽约州立大学布法罗分校计算机科学与工程系）
Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科交叉前沿研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology（北京理工大学天基智能信息处理全国重点实验室）
本文提出统一视觉-语言模型 Popeye，通过统一标注、混合专家编码、视觉-语言对齐与指令适配实现遥感多源船舶检测，并集成 SAM 支持像素级分割。
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest University School of Medicine（维克森林大学医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学综合科学与工程研究生院）；Dept. of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Dept. of Instrumentation and Control Engineering, NIT Trichy（印度国立理工学院蒂鲁奇拉帕利分校仪器与控制工程系）；Dept. of Otolaryngology, Singapore General Hospital（新加坡中央医院耳鼻喉科）
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）
