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Notice of Violation of IEEE Publication Principles: Mobile vision robot using the SIFT algorithm for object recognition

Notice of Violation of IEEE Publication Principles: Mobile v...

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International conference on Ultra Modern Telecommunications and workshops (ICUMT)

作者： Jin-Suk Kang Joongnam Jeon Youngeun An Jongan Park School of Electrical & Computer Engineering Chungbuk National University South Korea Department of Information & Communications Engineering Chosun University South Korea

Notice of Violation of IEEE Publication Principles "Mobile vision Robot Using the SIFT Algorithm for Object recognition" by Jin-Suk Kang, Joongnam Jeon, Youngeun An, and Jongan Park in the Proceedings of the 2009 International conference on Ultra Modern Telecommunications and workshops (ICUMT) Oct 2009 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "Leader/Follower Behaviour Using the SIFT Algorithm for Object recognition" by Jared Giesbrecht in Technical Memorandum, Defense R&D Canada, Suffield TM 2006-108 2006 This paper presents an application of vision-based object recognition to create in mobile robots. A system is developed which makes use of the scale invariant feature transform (SIFT) algorithm to recognize a leader robot or human. The follower robot then uses PID control to track the leader's movements while maintaining a fixed following distance. These experiments proved the SIFT algorithm as a viable method of creating leader/follower behavior, and can serve as a proof of concept for more complex convoying operations using machine-vision based leader detection.

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CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Karim Guirguis Ahmed Hendawy George Eskandar Mohamed Abdelsamad Matthias Kayser Jü rgen Beyerer Robert Bosch GmbH University of Stuttgart Karlsruhe Institute of Technology Fraunhofer IOSB

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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Population Shape Collapse in Large Deformation Registration of MR Brain Images

Population Shape Collapse in Large Deformation Registration ...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Shao Gary E. Christensen Hans J. Johnson Joo H. Song Oguz C. Durumeric Casey P. Johnson Joseph J. Shaffer Vincent A. Magnotta Jess G. Fiedorowicz John A. Wemmie Department of Electrical and Computer Engineering University of Iowa Iowa City lA USA Department of Mathematics University of Iowa Iowa City IA USA Department of Radiology University of Iowa Iowa City IA USA Department of Psychiatry University of Iowa Iowa City IA USA

This paper examines the shape collapse problem that occurs when registering a pair of images or a population of images of the brain to a reference (target) image coordinate system using diffeomorphic image registration. Shape collapse occurs when a foreground or background structure in an image with non-zero volume is transformed into a set of zero or near zero volume as measured on a discrete voxel lattice in the target image coordinate system. Shape collapse may occur during image registration when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image[4]. Such a problem is common in image registration algorithms with large degrees of freedom such as many diffeomorphic image registration algorithms. Shape collapse is a concern when mapping functional data. For example, loss of signal may occur when mapping functional data such as fMRI, PET, SPECT using a transformation with a shape collapse if the functional signal occurs at the collapse region. This paper proposes an novel shape collapse measurement algorithm to detect the regions of shape collapse after image registration in pairwise registration. We further compute the shape collapse for a population of pairwise transformations such as occurs when registering many images to a common atlas coordinate system. Experiments are presented using the SyN diffeomorphic image registration algorithm. We demonstrate how changing the input parameters to the SyN registration algorithm can mitigate some of the collapse image registration artifacts.

关键词： Shape Image registration Brain Lattices Shape measurement Correlation Urban areas

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Bracketing Image Restoration and Enhancement with High-Low F...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Genggeng Chen Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yongqing Yang Wei Dong Peng Wu Yanning Zhang Qingsen Yan Xi’an University of Architecture and Technology Northwestern Polytechnical University University of Macau Xi’an Institute of Optics and Precision Mechanics of CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration.

关键词： Degradation computer vision conferences Feature extraction Image restoration pattern recognition Image enhancement

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Siddeshwar Raghavan Jiangpeng He Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online 1

关键词： Continuing education Training computer vision Incremental learning conferences Contrastive learning Data models

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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Facial Expression recognition using Auto-regressive Models

Facial Expression Recognition using Auto-regressive Models

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International conference on pattern recognition

作者： F. Dornaika F. Davoine Computer Vision Center Barcelona Spain HEUDIASYC Mixed Research Unit CNRS/UTC Compiegne France

In this paper, we address the analysis and recognition of facial expressions in continuous videos. We introduce a view- and texture-independent approach that exploits the temporal facial action parameters estimated by an appearance-based 3D face tracker. The facial expression recognition is carried out using learned dynamical models based on auto-regressive processes. These learned models can also be utilized for the synthesis and prediction tasks. Experiments demonstrated the effectiveness of the developed method

关键词： Face recognition Shape Eyebrows Emotion recognition Deformable models Hidden Markov models Head computer vision Videos Parameter estimation

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Beyza Ermis Giovanni Zappella Martin Wistuba Aditya Rawal Cé dric Archambeau AWS Berlin AWS Santa Clara

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

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