Reliable motion forecasting of surrounding agents is essential for ensuring the safe operation of autonomous vehicles. Many existing trajectory prediction methods rely heavily on high-definition (HD) maps as strong driving priors. However, the availability and accuracy of these priors are not guaranteed due to substantial costs to build, localization errors of vehicles, or ongoing road constructions. In this paper, we introduce MFTP, a Map-Free Trajectory Prediction method that offers several advantages. First, it eliminates the need for HD maps during inference while still benefiting from map priors during training via knowledge distillation. Second, we present a novel hierarchical encoder that effectively extracts spatial-temporal agent features and aggregates them into multiple trajectory queries. Additionally, we introduce an iterative decoder that sequentially decodes trajectory queries to generate the final predictions. Extensive experiments show that our approach achieves state-of-the-art performance on the Argoverse dataset under the map-free setting.
Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Peking University（北京大学）；Alibaba Group（阿里巴巴集团）
Progressive trajectory matching for medical dataset distillation
National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究院）
OsteoFlow: Lyapunov-Guided Flow Distillation for Predicting Bone Remodeling after Mandibular Reconstruction
Department of Electrical and Computer Engineering, University of British Columbia, Canada（电气与计算机工程系，不列颠哥伦比亚大学，加拿大）；Department of Mechanical Engineering, University of British Columbia, Canada（机械工程系，不列颠哥伦比亚大学，加拿大）；Department of Surgery, University of British Columbia, Canada（外科系，不列颠哥伦比亚大学，加拿大）；Sunnybrook Research Institute, University of Toronto, Canada（阳光医院研究学院，多伦多大学，加拿大）
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
National University of Singapore（新加坡国立大学）；Massachusetts Institute of Technology（麻省理工学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Xidian University（西安电子科技大学）
Hefei University of Technology（合肥工业大学）；Jianghuai Advance Technology Center（江淮先进技术中心）；Anhui Provincial Key Laboratory of Humanoid Robots（安徽省人形机器人重点实验室）；Kaiyang Laboratory, Chery（凯阳实验室，中国奇瑞）；Zhejiang University of Technology（浙江工业大学）
Robust Dataset Distillation by Matching Adversarial Trajectories
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Microsoft Corporation（微软公司）；University of Wollongong（卧龙岗大学）
本文提出鲁棒数据集蒸馏任务及 MAT 方法，通过在轨迹生成中引入对抗样本，使自然训练的模型兼具准确率与对抗鲁棒性。
Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心（CFAR））；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所（IHPC））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thus recently come to the fore. This paradigm involves distilling information from large real-world datasets into tiny and compact synthetic datasets such that processing the latter ideally yields similar performances as the former. State-of-the-art methods primarily rely on learnin…
Elucidating the Preconditioning in Consistency Distillation
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, THBI Lab（计算机科学与技术系，人工智能研究所，BNRist中心，THBI实验室）；Tsinghua-Bosch Joint ML Center, Tsinghua University（清华大学-博世联合机器学习中心，清华大学）；Shengshu Technology（盛舒科技）；Pazhou Lab (Huangpu), Guangzhou, China（黄埔实验室（广州），中国）
University of Modena and Reggio Emilia（摩德纳雷焦艾米利亚大学）；University of Padova（帕多瓦大学）
Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this …
Department of Information Engineering Hong Kong；Department of Information Engineering
Since the wide employment of deep learning frameworks in video salient object detection, the accuracy of the recent approaches has made stunning progress. These approaches mainly adopt the sequential modules, based on optical flow or recurrent neural network (RNN), to learn robust spatiotemporal features. These modules are effective but significantly increase the computational burden of the corresponding deep models. In this paper, to simplify the network and maintain the accuracy, we present a lightweight network tailored for video salient object detection through the spatiotemporal knowledge…
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent prediction. Unlike previous methods that having adopting either an online or offline model, our single online model takes advantage of both models by distilling offline knowledge. To transfer knowledge correctly, we propose query filtering and association (QFA), which filters irrelevant queries to exact instances. Our KD with QFA increases the robustness of feature matching by encodin…
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping
South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Beijing Institute of Technology（北京理工大学）；The University of Sydney（悉尼大学）
Beijing University of Posts and Telecommunications（北京邮电大学）；ByteDance（字节跳动）；USTC（中国科学技术大学）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部互动技术与体验系统重点实验室）；Zhongguancun Academy（中关村科学城）
SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Sun Yat-sen University（中山大学）；Great Bay University（大贝大学）；Pazhou Lab (Huangpu)（琶洲实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection
ZongmuTech（宗目科技）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；Huazhong University of Science and Technology（华中科技大学）
Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）；School of Sports Training, Tianjin University of Sport（天津体育学院运动训练学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Electronic Science, National University of Defense Technology（国防科技大学电子科学学院）；School of Automation, Southeast University（东南大学自动化学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
Xi’an Jiaotong University（西安交通大学）；Chang’an University（长安大学）；The University of Sydney（悉尼大学）
Trajectory forecasting is critical for autonomous platforms to make safe planning and actions. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose a trajectory predictor directly based on detection results …
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Stuttgart（斯图加特大学）
This paper presents a novel approach to pedestrian trajectory prediction for on-board camera systems, which utilizes behavioral features of pedestrians that can be inferred from visual observations. Our proposed method, called Behavior-Aware Pedestrian Trajectory Prediction (BA-PTP), processes multiple input modalities, i.e. bounding boxes, body and head orientation of pedestrians as well as their pose, with independent encoding streams. The encodings of each stream are fused using a modality attention mechanism, resulting in a final embedding that is used to predict future bounding boxes in t…
V2X-RECT: An Efficient V2X Trajectory Prediction Framework via Redundant Interaction Filtering and Tracking Error Correction
Communication Research Center, Harbin Institute of Technology（哈尔滨工业大学通信研究所）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）电子与信息工程学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents. In this article, we present an asymmetrical bidirectional recurrent neural network architecture called U-RNN to encode pedestrian trajectories and evaluate its relevance to replace LSTMs for various forecasting models. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant yields better results regarding every available metrics (ADE, FDE, Collision rate) than common trajectory encoders for a variety of approaches and interaction modules, suggesting that the pro…
