Visual active search (VAS) has been introduced as a modeling framework that leverages visual cues to direct aerial (e.g., UAV-based) exploration and pinpoint areas of interest within extensive geospatial regions. Potential applications of VAS include detecting hotspots for rare wildlife poaching, aiding search-and-rescue missions, and uncovering illegal trafficking of weapons, among other uses. Previous VAS approaches assume that the entire search space is known upfront, which is often unrealistic due to constraints such as a restricted field of view and high acquisition costs, and they typically learn policies tailored to specific target objects, which limits their ability to search for multiple target categories simultaneously. In this work, we propose DiffVAS, a target-conditioned policy that searches for diverse objects simultaneously according to task requirements in partially observable environments, which advances the deployment of visual active search policies in real-world applications. DiffVAS leverages a diffusion model to reconstruct the entire geospatial area from sequentially observed partial glimpses, which enables a target-conditioned reinforcement learning-based planning module to effectively reason and guide subsequent search steps. Extensive experiments demonstrate that DiffVAS excels in searching diverse objects in partially observable environments, significantly surpassing state-of-the-art methods on several datasets.
Pose Guided Human Image Synthesis with Partially Decoupled GAN
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）；University of Science and Technology of China（中国科学技术大学）；Shanghai International Studies University（上海外国语大学）
Recovering Partially Corrupted Objects via Sketch-Guided Bidirectional Feature Interaction
School of Electrical and Data Engineering（电气与数据工程学院）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Hefei University of Technology（合肥工业大学）
Imagine Before Concentration: Diffusion-Guided Registers Enhance Partially Relevant Video Retrieval
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
The University of Texas at Austin（得克萨斯大学奥斯汀分校）；Radboud University（拉德堡德大学）；Albert-Ludwigs-Universität Freiburg（阿尔伯特-路德维希大学弗莱堡分校）；Concept Engineering GmbH（康赛普特工程有限公司）
Expert-Guided POMDP Learning for Data-Efficient Modeling in Healthcare
Department of Informatics, Systems and Communication, University of Milano-Bicocca（信息学、系统与通信系，米兰-比科卡大学）；Department of Computer Science, Open University（计算机科学系，开放大学）；Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano-Bicocca（比科卡生物信息学、生物统计学与生物成像中心-B4，米兰-比科卡大学）；BReCHS – Bicocca Research Centre in Health Services, University of Milano-Bicocca（比科卡健康服务研究中心，米兰-比科卡大学）
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Tufts University（塔夫茨大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Georgia Tech Research Institute（佐治亚理工学院研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation
Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Department of Radiation Oncology, Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心放射肿瘤科）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Herbert Irving Comprehensive Cancer Center, Columbia University（哥伦比亚大学赫伯特·欧文综合癌症中心）；Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院放射肿瘤科）
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning
Department of Aerospace Engineering（航空航天工程系）；University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Department of Mathematics（数学系）；Engineer Research and Development Center（工程研究与发展中心）；U.S. Army Corps of Engineers（美国陆军工程兵团）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Massachusetts: Dartmouth（马萨诸塞大学达特茅斯分校）；United States Army Research Laboratory（美国陆军研究实验室）
DQ3D: Depth-guided Query for Transformer-Based 3D Object Detection in Traffic Scenarios
The School of Computer Science and Engineering, BUAA, Beijing, China（北京航空航 大学计算机科学与工程学院）；The School of Computer Science and Engineering, The University of Sydney, Sydney, Australia（悉尼大学计算机科学与工程学院）
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
The Chinese University of Hong Kong, HKSAR（香港中文大学）；TReNDS Center (Georgia State, Georgia Tech, Emory)（TReNDS中心（佐治亚州立大学、佐治亚理工学院、埃默里大学））；ShanghaiTech University, Shanghai, P.R.China（上海科技大学）；University of California, Berkeley, USA（加州大学伯克利分校）；University of Texas Health Science Center at Houston, USA（德克萨斯大学健康科学中心（休斯顿））；Nanyang Technological University, Singapore（南洋理工大学）
CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud
Wuhan University（武汉大学）
目前，基于锚点或无锚点的三维目标检测模型均采用中心分配器策略来推断三维边界框。然而，在现实场景中，激光雷达只能获取有限的物体表面点云，而物体的中心点是不存在的。通过聚集不完整的表面点云来获取目标，会带来方向和尺寸估计精度的损失。针对这一问题，本文提出了一种角点引导无锚点单级三维目标检测模型（CG-SSD），该模型首先利用由残差层和子流形稀疏卷积层组成的三维稀疏卷积骨干网络构建鸟瞰图（BEV）特征，然后利用精简的U型网络进行更深层次的特征挖掘;其次，提出了一种新的角点引导辅助模块（CGAM），将角点监控信号纳入神经网络。CGAM被明确地设计和训练来检测部分可见和不可见的角点，以获得更准确的对象特征表示，特别是对于小的或部分被遮挡的对象;最后，来自骨干网络和CGAM模块的深度特征被连接并馈送到头部模块，以预测场景中对象的分类和3D边界框。实验结果表明，CG-SSD在ONCE基准测试中使用单帧点云数据进行监督式3D目标检测，达到了最先进的性能，具有62.77%mAP。此外，在ONCE和Waymo Open Dataset上的实验表明，CGAM可以扩展到大多数基于锚点的模型，这些模型使用BEV特征来检测对象，作为插件，并带来+1.17%-+14.27%AP的改进。
