Deep learning has a wide range of applications in industrial scenario, but reducing false alarm (FA) remains a major difficulty. Optimizing network architecture or network parameters is used to tackle this challenge in academic circles, while ignoring the essential characteristics of data in application scenarios, which often results in increased FA in new scenarios. In this paper, we propose a novel paradigm for fine-grained design of datasets, driven by industrial applications. We flexibly select positive and negative sample sets according to the essential features of the data and application requirements, and add the remaining samples to the training set as uncertainty classes. We collect more than 10,000 mask-wearing recognition samples covering various application scenarios as our experimental data. Compared with the traditional data design methods, our method achieves better results and effectively reduces FA. We make all contributions available to the research community for broader use. The contributions will be available at https://github.com/huh30/OpenDatasets.
UC Berkeley（加州大学伯克利分校）；ICSI（国际计算机科学研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland University（萨尔大学）；Technische Universität München（慕尼黑工业大学）；Stanford University（斯坦福大学）；SPIEGEL-Verlag（明镜出版社）
Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-b…
NEC Labs America（美国NEC实验室）；University of Iowa（爱荷华大学）；University of Missouri（密苏里大学）
This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification. Unfortunately, CNN suffers from over-fiting when it is trained on existing fine-grained image classification benchmarks, which typically only consist of less than a few tens of thousands training images. Therefore, we first construct a large-scale fine-grained car recognition dataset that consists of 333 car classes with more than 150 thousand training images. With this large-scale datas…
Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；Watrix Technology Limited Co. Ltd.（华瑞芯科技有限公司）
Beihang University（北京航空航天大学）；Sea AI Lab（Sea AI实验室）
Vision and language understanding techniques have achieved remarkable progress, but currently it is still difficult to well handle problems involving very fine-grained details. For example, when the robot is told to "bring me the book in the girl's left hand", most existing methods would fail if the girl holds one book respectively in her left and right hand. In this work, we introduce a new task named human-centric relation segmentation (HRS), as a fine-grained case of HOI-det. HRS aims to predict the relations between the human and surrounding entities and identify the relation-correlated hu…
VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models
Nankai University（南开大学）；Pengcheng Laboratory（鹏城实验室）；Kuaishou Technology（快手科技）；Nankai International Advanced Research Institute (SHENZHEN·FUTIAN)（南开国际先进研究院（深圳·福田））
EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation
ByteDance Intelligent Creation（字节跳动智能创作）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Merced（加州大学默塞德分校）；University of California, Los Angeles（加州大学洛杉矶分校）
Full-body portrait stylization, which aims to translate portrait photography into a cartoon style, has drawn attention recently. However, most methods have focused only on converting face regions, restraining the feasibility of use in real-world applications. A recently proposed two-stage method expands the rendering area to full bodies, but the outputs are less plausible and fail to achieve quality robustness of non-face regions. Furthermore, they cannot reflect diverse skin tones. In this study, we propose a data-centric solution to build a production-level full-body portrait stylization sys…
FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment
School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
Westlake University（西湖大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；The future network of intelligence institute (FNII)（未来网络智能研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M^2-Track. At the 1^st-stage,…
EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
X-Humanoid；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；University of Manchester（曼彻斯特大学）；Monash University（墨尔本大学）；Celonis AI；University of New South Wales（新南威尔士大学）
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
Mach Drive（马赫驱动）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Science and Technology of China（中国科学技术大学）
本文提出 SparseAD 稀疏查询中心范式，以统一稀疏架构替代稠密 BEV 完成感知、预测与规划，在 nuScenes 上达到端到端方法的 SOTA 全任务性能。
The University of Sydney（悉尼大学）；SenseTime Research（商汤科技研究院）；Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）
Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, i…
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
National University of Defense Technology（国防科技大学）；HKUST(GZ)（香港理工大学）；Qilu University of Technology（青岛科技大学）；Technical University of Munich（慕尼黑技术大学）；TeleAI, China Telecom（TeleAI，中国电信）；Beihang University（北航）
COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts
Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（中国科学院福建物质结构研究所）；Institute of Urban Environment, Chinese Academy of Sciences（中国科学院城市环境研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）
We contribute HAA500, a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames. To minimize ambiguities in action classification, HAA500 consists of highly diversified classes of fine-grained atomic actions, where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball". Thus HAA500 is different from existing atomic action datasets, where coarse-grained atomic actions were labeled with coarse action-verbs such as "Throw". HAA500 has been carefully curated to capture the precise…
EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Microsoft AI Superintelligence（微软人工智能超智实验室）；Lambda, Inc（Lambda公司）
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining
College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Hainan Institute, Zhejiang University（海南研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grain…
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
Om AI Research（Om AI研究机构）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Shifting AI Efficiency From Model-Centric to Data-Centric Compression
EPIC Lab, Shanghai Jiao Tong University（上海交通大学EPIC实验室）；Sichuan University（四川大学）；University of Electronic Science & Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sun Yat-sen University（中山大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
MotionAtlas: Detailed Region Captioning for Motion-Centric Videos
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；Wuhan University（武汉大学）；Beijing Jiaotong University（北京交通大学）
Towards the Vision-Sound-Language-Action Paradigm: The HEAR Framework for Sound-Centric Manipulation
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University and Shanghai Key Laboratory of Navigation and Location Based Services（自动化与智能感知学院，上海交通大学，导航与基于位置的服务重点实验室）；Department of Engineering, Cambridge University（工程系，剑桥大学）
