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Create Your World: Lifelong Text-to-Image Diffusion

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arXiv 2023年

作者： Sun, Gan Liang, Wenqi Dong, Jiahua Li, Jun Ding, Zhengming Cong, Yang State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu210094 China The Department of Computer Science Tulane University New OrleansLA70118 United States The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of Chinese Academy of Sciences Beijing100049 China

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a user's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

FDCT: Fast Depth Completion for Transparent Objects

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arXiv 2023年

作者： Li, Tianan Chen, Zhehan Liu, Huan Wang, Chen The School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China The Department of Electrical and Computer Engineering McMaster University Canada The Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://***/Nonmy/FDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

IoT based Truck Wheel Temperature Monitoring System

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IoT based Truck Wheel Temperature Monitoring System

Intelligent Technologies (ACOIT), Asian Conference on

作者： Mohd Zaid Nadeem Ahmad Mohammed Aljamal Sarosh Patel Asif Khan dept. of Computer Science and Engineering Integral University Lucknow India dept. of Electronics and communication Engg Goel Institute of Technology And Managment Lucknow India School of Engineering University of Bridgeport USA dept. of Computer Application Integral Robotics Lab Integral University Lucknow India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people.

关键词： Temperature measurement Temperature sensors Temperature distribution Vehicle safety Wheels Weather forecasting Transportation Real-time systems Monitoring Wireless fidelity

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

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CITR: A Coordinate-Invariant Task Representation for Robotic...

IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

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arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

A Robust Adaptive Terminal Sliding Mode Control for Lane-keeping in Road Vehicles

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A Robust Adaptive Terminal Sliding Mode Control for Lane-kee...

International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Zhikang Ge Zhuo Wang Xiaoping Bai Xiaoxiong Wang Zhihong Man Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

In this paper, a novel adaptive terminal sliding-mode control (ATSMC) method is developed for road vehicles with uncertain dynamics. It is shown that the designed adaptive laws can recursively update the controller parameters based on the estimation values of the unknown parameter bound in the Lyapunov sense, and then the lateral position error can be driven to zero in a finite time. In comparison to the conventional sliding-mode-based vehicle lateral control method, the proposed ATSMC not only ensures fast error convergence, but also eliminates the requirement for known bounded information in controller design. Simulation results are presented to demonstrate the good performance of the proposed ATSMC scheme.

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation

International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Xiaobo Liu Fang Wan Sheng Ge Haokun Wang Haoran Sun Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen Guangdong China School of Design Southern University of Science and Technology Shenzhen Guangdong China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong China Department of Computer Science The University of Hong Kong Hong Kong China

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.

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DropHD: technology/Algorithm Co-Design for Reliable Energy-Efficient NVM-Based Hyper-Dimensional Computing Under Voltage Scaling

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DropHD: Technology/Algorithm Co-Design for Reliable Energy-E...

Design, Automation and Test in Europe Conference and Exhibition

作者： Paul R. Genssler Mahta Mayahinia Simon Thomann Mehdi B. Tahoori Hussam Amrouch Semiconductor Test and Reliability University of Stuttgart Stuttgart Germany Department of Computer Science and Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of AI Processor Design TUM School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence Munich Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Brain-inspired hyperdimensional computing (HDC) offers much more efficient computing compared to other classical deep learning and related machine learning algorithms. Unlike classical CMOS, emerging non-volatile memories (NVMs) used in the realization of HDC are susceptible to failures under voltage scaling, which is essential for energy saving. Although HDC is inherently robust against errors, this is only possible when hypervectors with a large dimension (e.g., 10,000 bits) are being used, resulting in significant energy consumption. This work demonstrates, for the first time, that different NVM technologies exhibit different error characteristics under voltage scaling. In contrast to conventional CMOS-based SRAM, we demonstrate that the error behavior is data-dependent and not captured by simple bit flips in emerging NVMs. We employ our cross-layer framework that starts from the underlying technology all the way up to the algorithm to develop the novel HDC training approach DropHD. DropHD considerably shrinks the size of hypervectors (e.g., from 10,000 bits down to merely 3000 bits), while maintaining a high inference accuracy. The use of aggressive voltage scaling reduces energy consumption by 1.6 x. DropHD further reduces it to up to 9.5 × while fully recovering the induced accuracy drop, i.e. without a tradeoff.

关键词： Training Energy consumption Machine learning algorithms Nonvolatile memory Random access memory Voltage Inference algorithms