Roads have well defined geometries, topologies, and traffic rules. While this has been widely exploited in motion planning methods to produce maneuvers that obey the law, little work has been devoted to utilize these priors in perception and motion forecasting methods. In this paper we propose to incorporate these structured priors as a loss function. In contrast to imposing hard constraints, this approach allows the model to handle non-compliant maneuvers when those happen in the real world. Safe motion planning is the end goal, and thus a probabilistic characterization of the possible future developments of the scene is key to choose the plan with the lowest expected cost. Towards this goal, we design a framework that leverages REINFORCE to incorporate non-differentiable priors over sample trajectories from a probabilistic model, thus optimizing the whole distribution. We demonstrate the effectiveness of our approach on real-world self-driving datasets containing complex road topologies and multi-agent interactions. Our motion forecasts not only exhibit better precision and map understanding, but most importantly result in safer motion plans taken by our self-driving vehicle. We emphasize that despite the importance of this evaluation, it has been often overlooked by previous perception and motion forecasting works.
VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃朗根-纽伦堡大学）；University of Maryland School of Dentistry（马里兰大学牙科学院）；Harvard Medical School/Massachusetts General Hospital（哈佛医学院/麻省总医院）；Universidad de Antioquia UdeA（安蒂奥基亚大学 UdeA）；Johns Hopkins University（约翰霍普金斯大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation
Dept. of Pathology, University Medical Center Utrecht（病理学系，乌得勒支大学医学中心）；Dept. of Biomedical Engineering, Eindhoven University of Technology（生物医学工程系，埃因霍温理工大学）；Dept. of Mathematics and Computer Science, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）
Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Faculty of Computer Science, AGH University of Krakow（计算机科学系，克拉科夫AGH大学）；Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta（信息系，全国 veterans 大学 Yogya 市）
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The Hong Kong University of Science and Technology（香港科技大学）；The Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Sichuan Province Cancer Hospital, University of Electronic Science and Technology of China（电子科技大学四川省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）
Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs
New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)（模式识别新实验室、多模态人工智能系统国家重点实验室、自动化研究所、中国科学院（CASIA））；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；Wuhan University（武汉大学）；ByteDance（字节跳动）
Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset
Shanghai Center for Brain Science and Brain-Inspired Technology（上海脑科学与类脑研究中心）；NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai（上海纽约大学纽约大学-华东师范大学脑与认知科学研究院）
Multimodal Slice Interaction Network Enhanced by Transfer Learning for Precise Segmentation of Internal Gross Tumor Volume in Lung Cancer PET/CT Imaging
Dept. of Biomedical Engineering Johns Hopkins University（生物医学工程系约翰霍普金斯大学）；Molecular Radiation Sciences Johns Hopkins University（分子放射科学约翰霍普金斯大学）；Dept. of Surgery University of Minnesota（外科系明尼苏达大学）；Dept. of Biomedical Engineering University of Virginia（生物医学工程系弗吉尼亚大学）；Comprehensive Cancer Center Mayo Clinic（综合癌症中心梅奥诊所）
MM-Gesture: Towards Precise Micro-Gesture Recognition through Multimodal Fusion
University College London (UCL)（伦敦大学学院）；School of Computer Science（计算机科学学院）；Information Engineering, School of Artificial Intelligence, Hefei University of Technology (HFUT)（信息工程学院，人工智能学院，合肥工业大学）；ReLER, CCAI, Zhejiang University, China（ReLER、CCAI、浙江大学，中国）；Key Laboratory of Knowledge Engineering with Big Data (HFUT), Ministry of Education（大数据知识工程重点实验室（HFUT），教育部）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China（人工智能研究所，合肥综合性国家科学中心，中国）；Xinsight Lab, Research Institute, Hefei Zhongjuyuan Intelligent Technology Co., Ltd., China（Xinsight实验室，研究院，合肥中睿智能科技有限公司，中国）
MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)
Skolkovo Institute of Science and Technology (Skoltech)（斯克罗夫诺科学与技术学院（Skoltech））；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection
Beijing University of Technology（北京工业大学）；Southwest University（西南大学）；China University of Geosciences Wuhan（中国地质大学（武汉））；Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Yunnan Normal University（云南师范大学）；Beijing Forestry University（北京林业大学）；Ghent University（根特大学）
