With the development of Augmented Reality (AR), egocentric action recognition (EAR) plays important role in accurately understanding demands from the user. However, EAR is designed to help recognize human-machine interaction in single egocentric view, thus difficult to capture interactions between two face-to-face AR users. Paired egocentric interaction recognition (PEIR) is the task to collaboratively recognize the interactions between two persons with the videos in their corresponding views. Unfortunately, existing PEIR methods always directly use linear decision function to fuse the features extracted from two corresponding egocentric videos, which ignore consistency of interaction in paired egocentric videos. The consistency of interactions in paired videos, and features extracted from them are correlated to each other. On top of that, we propose to build the relevance between two views using biliear pooling, which capture the consistency of two views in feature-level. Specifically, each neuron in the feature maps from one view connects to the neurons from another view, which guarantee the compact consistency between two views. Then all possible paired neurons are used for PEIR for the inside consistent information of them. To be efficient, we use compact bilinear pooling with Count Sketch to avoid directly computing outer product in bilinear. Experimental results on dataset PEV shows the superiority of the proposed methods on the task PEIR.
University of Chinese Academy of Sciences（中国科学院大学）；City University of Hong Kong（香港城市大学）；South China University of Technology（华南理工大学）
Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition and social interaction understanding. In this …
Carnegie Mellon University（卡内基梅隆大学）；Inria（法国国家信息与自动化研究所）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
Several theories in cognitive neuroscience suggest that when people interact with the world, or simulate interactions, they do so from a first-person egocentric perspective, and seamlessly transfer knowledge between third-person (observer) and first-person (actor). Despite this, learning such models for human action recognition has not been achievable due to the lack of data. This paper takes a step in this direction, with the introduction of Charades-Ego, a large-scale dataset of paired first-person and third-person videos, involving 112 people, with 4000 paired videos. This enables learning …
Carnegie Mellon University（卡内基梅隆大学）；Inria（法国国家信息与自动化研究所）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
In Actor and Observer we introduced a dataset linking the first and third-person video understanding domains, the Charades-Ego Dataset. In this paper we describe the egocentric aspect of the dataset and present annotations for Charades-Ego with 68,536 activity instances in 68.8 hours of first and third-person video, making it one of the largest and most diverse egocentric datasets available. Charades-Ego furthermore shares activity classes, scripts, and methodology with the Charades dataset, that consist of additional 82.3 hours of third-person video with 66,500 activity instances. Charades-Eg…
BinoGen: Scaling egocentric binocular data for embodied visual perception and learning
College of Biological Sciences, China Agricultural University（中国农业大学生物学院）；Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院北京脑研究所）；Chinese Institute for Brain Research, Beijing (CIBR)（北京脑科学与类脑研究中心）
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）
In this paper, we propose a novel approach to enhance the 3D body pose estimation of a person computed from videos captured from a single wearable camera. The key idea is to leverage high-level features linking first- and third-views in a joint embedding space. To learn such embedding space we introduce First2Third-Pose, a new paired synchronized dataset of nearly 2,000 videos depicting human activities captured from both first- and third-view perspectives. We explicitly consider spatial- and motion-domain features, combined using a semi-Siamese architecture trained in a self-supervised fashio…
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model
College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Computer Vision Center（计算机视觉中心）；Autonomous University of Barcelona（巴塞罗那自治大学）；Amsterdam Machine Learning Lab（阿姆斯特丹机器学习实验室）；University of Amsterdam（阿姆斯特丹大学）
Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains …
PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting
School of Computer Science, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳校区计算机科学学院，中国）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室，中国）；Smart Coding Institute, Pengcheng Laboratory, Shenzhen, China（鹏城实验室智能编码研究所，中国）；School of Computer Science, Harbin Institute of Technology, Harbin, China（哈尔滨工业大学计算机科学学院，中国）
Estimating camera wearer's body pose from an egocentric view (egopose) is a vital task in augmented and virtual reality. Existing approaches either use a narrow field of view front facing camera that barely captures the wearer, or an extruded head-mounted top-down camera for maximal wearer visibility. In this paper, we tackle the egopose estimation from a more natural human vision span, where camera wearer can be seen in the peripheral view and depending on the head pose the wearer may become invisible or has a limited partial view. This is a realistic visual field for user-centric wearable de…
Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining
Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology（广东极化成像与测量工程研究中心）；Shenzhen Key Laboratory for Minimal Invasive Medical Technologies（深圳微创医疗技术重点实验室）；Institute of Biopharmaceutical and Health Engineering（生物制药与健康工程研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际 Graduate School）；Tsinghua University（清华大学）
Color-based two-hand 3D pose estimation in the global coordinate system is essential in many applications. However, there are very few datasets dedicated to this task and no existing dataset supports estimation in a non-laboratory environment. This is largely attributed to the sophisticated data collection process required for 3D hand pose annotations, which also leads to difficulty in obtaining instances with the level of visual diversity needed for estimation in the wild. Progressing towards this goal, a large-scale dataset Ego2Hands was recently proposed to address the task of two-hand segm…
