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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

arXiv

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arXiv 2023年

作者： Liu, Yuyang Cong, Yang Goswami, Dipam Liu, Xialei van de Weijer, Joost State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China South China University of Technology China Computer Vision Center Barcelona Spain VCIP CS Nankai University China Department of Computer Science Universitat Autònoma de Barcelona Spain

In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model 1 © 2023, CC BY.

关键词： Object detection

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on Electronic Information Engineering and computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Bidirectional Search Strategy for Incremental Search-based Path Planning

Bidirectional Search Strategy for Incremental Search-based P...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chenming Li Han Ma Jiankun Wang Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong SAR China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Per-ception and Intelligence Southern University of Science and Technology Shenzhen China Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Department of Electrical and Computer Engineering University of Alberta in Canada

Planning a collision-free path efficiently among obstacles is crucial in robotics. Conventional one-shot unidirectional path planning algorithms work well in the static environment, but cannot respond to the environment changes timely in the dynamic environment. To tackle this issue and improve the search efficiency, we propose a bidirectional incremental search method, Bidirectional Lifelong Planning A* (BLPA*), which searches in the forward and backward directions and performs incremental search bidirectionally when the environment changes. Furthermore, inspired by the robot perception range limitation and BLPA*, we propose the fractional bidirectional D* Lite (fBD* Lite(d p )), which constraints the forward search to the robot perception range and uses the backward search to expand the rest area. Our simulation results demonstrate BLPA* and mD* Lite(d p ) can achieve superior performance in the dynamic environment. It reveals that the bidirectional incremental search strategy can be a general and efficient technique for graph-search-based robot path planning methods.

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MUCOR: A Multiparty Conversation Based Robotic Interface to Evaluate Job Applicants 22nd

MUCOR: A Multiparty Conversation Based Robotic Interface to ...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Senaratna, H.A.S.D. Manawadu, Udaka A. Hansika, W.K.N. Samarasinghe, S.W.A.M.D. De Silva, P. Ravindra S. Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Robot Engineering Laboratory Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9783030601164

"MUCOR" is a robotic interface made up of three small-sized, table-top robots that go through Linkedin profiles of job applicants and discuss the applicants’ information. Robots categorize applicants into ordered levels based on their competencies mentioned. Robots built following minimalist design concepts consist of a cylindrical head and a body that occupy less space. A score is calculated for each candidate and categorized into levels by a model built including Agglomerative Clustering and Gaussian Mixture Model. The model itself identifies the number of levels into which the applicants can be categorized, through the iterations. MUCOR is built for job recruiters who spend more time to find background information about applicants prior to an interview. Conversation among robots and the user creates a Passive Social Interface in which the user does not involve in the conversation, but gain knowledge by listening. The recruiter only has to input the LinkedIn IDs into a mobile application and listen to the conversation among robots. MUCOR was experimented with professional recruiters and their feedback highlighted that the synchronization of speech with the robot’s body movements has a fair impact on the user experience about the robot behavior. © 2020, Springer Nature Switzerland AG.

关键词： Human robot interaction

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A Sociable Robotic Platform to Make Career Advices for Undergraduates 22nd

A Sociable Robotic Platform to Make Career Advices for Under...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Mithsara, W. K. Malithi Manawadu, Udaka A. De Silva, P. Ravindra S. Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Robot Engineering Laboratory Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9783030601164

Most of the undergraduates couldn’t figure out a proper direction for their future careers since advising depends on the person and students show aversion in revealing their information to other humans. This research focused on creating a social robotic platform to interact with undergraduates in the field of computer science to realize possible career paths, as recent researches show social companion robots tend to form a stronger bond, which helps the addressee to share their information easily. The robot was created as a tabletop robot employing minimalistic design with a friendly view attached with speech synthesis for communicating purposes. Data was collected from 202 persons, who followed a degree in computer science. Data contains their experience, qualifications, and skills. Thereafter an artificial neural network was created using supervised learning to predict the career path with 95% performance accuracy. The experiment was performed by engaging 15 students from the final year who are a doing degree in computer science and they were asked to provide feedback on the interactions with the robot. The gathered responses highlighted robot animacy, interaction, technology and usefulness. Therefore, from the results it can be concluded that a majority of the students accepted the robot, interacted without hesitation and had friendly conversations with the robot where they valued the generated output from the robot. © 2020, Springer Nature Switzerland AG.

关键词： Neural networks

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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Fast-moving object counting with an event camera

arXiv

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arXiv 2022年

作者： Bialik, Kamil Kowalczyk, Marcin Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed. © 2022, CC BY.

关键词： Cameras

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PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

TechRxiv

引用

TechRxiv 2022年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars backbone. © 2022, CC BY.

关键词： Object detection

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