Manipulation tasks in daily life, such as pouring water, unfold intentionally under specialized manipulation contexts. Being able to process contextual knowledge in these Activities of Daily Living (ADLs) over time can help us understand manipulation intentions, which are essential for an intelligent robot to transition smoothly between various manipulation actions. In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner. Furthermore, we propose a scheme to generate a combination of visual attentions and an evolving knowledge graph filled with commonsense knowledge. Our scheme works with real-world camera streams and fuses an attention-based Vision-Language model with the ontology system. The experimental results demonstrate that the proposed scheme can successfully represent the evolution of an intended object manipulation procedure for both robots and humans. The proposed scheme allows the robot to mimic human-like intentional behaviors by watching real-time videos. We aim to develop this scheme further for real-world robot intelligence in Human-Robot Interaction.
Understanding manipulation scenarios allows intelligent robots to plan for appropriate actions to complete a manipulation task successfully. It is essential for intelligent robots to semantically interpret manipulation knowledge by describing entities, relations and attributes in a structural manner. In this paper, we propose an implementing framework to generate high-level conceptual dynamic knowledge graphs from video clips. A combination of a Vision-Language model and an ontology system, in correspondence with visual perception and contextual semantics, is used to represent robot manipulati…
UniWAM Technical Report: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision
Physical Superintelligence Lab, Fysics AI（Fysics AI 物理超级智能实验室）；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）
The University of North Carolina（北卡罗来纳大学）；City University of Hong Kong（香港城市大学）
In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object with unknown deformation parameters. The servo-control is accomplished by learning a feedback controller that determines the robotic end-effector's movement according to the deformable object's current status. This status encodes the object's deformation behavior by using a set of observed visual features, which are either manually designed or automatically extracted from the robot's sensor stream. A feedback control policy is then optimized to push t…
A Multi-View 3D Telepresence System for XR Robot Teleoperation
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人类学与机器人研究所）；Institute of Human and Industrial Engineering, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人因与工业工程研究所）
Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework.…
University of Maryland, College Park（马里兰大学帕克分校）；Adobe Research（Adobe研究院）
Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. We propose a two-stream Faster R-CNN network and train it endto- end to detect the tampered regions given a manipulated image. One of the two streams is an RGB stream whose purpose is to extract features from the RGB image input to find tampering artifacts like strong contrast difference, unnatural tampered boundaries, and so on. The other is a noise stream that leverages the noi…
Purdue University（普渡大学）；Politecnico di Milano（米兰理工大学）
Manipulating video content is easier than ever. Due to the misuse potential of manipulated content, multiple detection techniques that analyze the pixel data from the videos have been proposed. However, clever manipulators should also carefully forge the metadata and auxiliary header information, which is harder to do for videos than images. In this paper, we propose to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers, completely avoiding the pixel space. Using well-known datasets, our results show that this scalable approach can achieve a …
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the b…
The Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Ant Group（蚂蚁集团）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）
Center for Research on Intelligent Perception and Computing, NLPR, CASIA（中国科学院自动化研究所模式识别国家重点实验室智能感知与计算研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So this paper proposes the first free-style text-to-face method namely AnyFace enabling much wider open world applications such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel two-stream framework for face image synthesis and manipulation given arbitrary descriptions of the human face. Specifically, one stream performs text-to-face generation and the other conducts face image reconstruction. …
GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Media Lab and EECS（媒体实验室和电子工程与计算机科学系）；MIT（麻省理工学院）；Princeton University（普林斯顿大学）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Georg-August-Universität Göttingen（格奥尔格-奥古斯特-哥廷根大学）
Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and complex manipulation action tasks, such as "preparing a breakfast" or "making a sandwich". We represent manipulations with our recently introduced "Semantic Event Chain" (SEC) concept, which captures th…
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）
DeepAgent: A Dual Stream Multi Agent Fusion for Robust Multimodal Deepfake Detection
Applied Artificial Intelligence and Intelligent Systems (AAIINS) Laboratory（应用人工智能与智能系统实验室）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Scholars（学者大学）；Faculty of Science and Information Technology（科学与信息技术学院）；Faculty of Science and Technology（科学与技术学院）
How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding nece…
University of Maryland（马里兰大学）；Australian National University（澳大利亚国立大学）；University of Granada（格拉纳达大学）；University of Zurich（苏黎世大学）；ETH Zürich（苏黎世联邦理工学院）
Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flippin…
