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Online Unsupervised Domain Adaptation for Person Re-identification

Online Unsupervised Domain Adaptation for Person Re-identifi...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hamza Rami Matthieu Ospici Sté phane Lathuiliè re LTCI T&#x00E9 l&#x00E9 com Paris Institut Polytechnique de Paris Atos

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network’s cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.

关键词： Training Adaptation models Privacy computer vision conferences Benchmark testing Data models

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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anthony Cioppa Silvio Giancola Adrien Deliè ge Le Kang Xin Zhou Zhiyu Cheng Bernard Ghanem Marc Van Droogenbroeck University of Li&#x00E8 ge KAUST Baidu Research

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations

PEA: Improving the Performance of ReLU Networks for Free by ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Á kos Utasi Continental AI Development Center Budapest

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where the availability of complex activations is limited, and usually only the ReLU is supported. In this paper we propose methods that can be used to improve the performance of ReLU networks by using these efficient novel activations during model training. More specifically, we propose ensemble activations that are composed of the ReLU and one of these novel activations. Furthermore, the coefficients of the ensemble are neither fixed nor learned, but are progressively updated during the training process in a way that by the end of the training only the ReLU activations remain active in the network and the other activations can be removed. This means that in inference time the network contains ReLU activations only. We perform extensive evaluations on the ImageNet classification task using various compact network architectures and various novel activation functions. Results show 0.2–0.8% top-1 accuracy gain, which confirms the applicability of the proposed methods. Furthermore, we demonstrate the proposed methods on semantic segmentation and we boost the performance of a compact segmentation network by 0.34% mIOU on the Cityscapes dataset.

关键词： Training Image segmentation computer vision conferences Semantics Neural networks Network architecture

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Himalaya Jain Tuan-Hung Vu Patrick Pé rez Matthieu Cord Valeo.ai Paris France Sorbonne University Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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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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Implementing TANAGRA for the Visualization and Analytical Interpretation of E-commerce Datasets 2

Implementing TANAGRA for the Visualization and Analytical In...

引用

2nd ieee International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Khan, Gulista Karthikeyan, M.P. College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India University Karnataka Bangalore India

ISBN: (纸本)9798350369960

The multidisciplinary study of data mining is crucial to artificial intelligence and computer science because it allows researchers to glean useful insights from massive data sets;these datasets are essential to online shopping platforms. This field investigates the ways in which data mining. Consumer impression analysis, a rich source of qualitative data reflecting customer attitude, is the center of this study. Researchers use sophisticated natural language processing algorithms to sift through customer reviews in search of patterns and key opinion leaders. In order to provide complex insights on customer behavior, this study dives into the world of sentiment analysis and goes beyond just gathering comments. Unstructured textual data may be dynamically analyzed using ML approaches in conjunction with NLP. This method can automatically decipher intricate client data. These methods, when put together, allow for the extraction of valuable information that are critical for the development of business strategies and the improvement of digital marketplace customer interaction models. An all-inclusive data mining technique known as TANAGRA is used in the investigation. It handles problems caused by the scale and variety of big e-commerce datasets via analysis. This study's findings provide insight into the nuances of consumer behavior and point the way toward better user experience and e-commerce strategy optimization. To better understand how customers, behave on e-commerce sites, the research employs Data Mining, ML, and NLP. As a result, computer vision is able to analyze more complex data patterns, and AI predictive analytics get fresh understanding. © 2023 ieee.

关键词： Consumer Behaviour Analysis E-commerce Data Visualization Sales Trend Interpretation TANAGRA Software Transaction pattern Graphing

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Trident Dehazing Network

Trident Dehazing Network

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jing Wu, Haiyan Xie, Yuan Qu, Yanyun Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9781728193601

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy-free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature;the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder;and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： computer vision

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Design and Prototyping of Computational Sunglasses for Autism Spectrum Disorders

Design and Prototyping of Computational Sunglasses for Autis...

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28th ieee conference on Virtual Reality and 3D User Interfaces (ieee VR)

作者： Hu, Xiaodan Zhang, Yan Isoyama, Naoya Sakata, Nobuchika Kiyokawa, Kiyoshi NAIST Ikoma Japan

ISBN: (纸本)9781665440578

We design the computational sunglasses for individuals with autism spectrum disorders (ASDs) to alleviate the atypical visual perceptions caused by their unique light sensitivities. With the proposed system, a scene camera is used for ambient illuminance detection, and an appropriate occlusion pattern is calculated in real-time then displayed on a spatial light modulator (SLM) to modulate the contrast of the incoming scene. Based on a simple contrast adjustment algorithm that avoids overexposure and underexposure, our benchtop prototype demonstrates better scene contrast successfully.

关键词： Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Computing methodologies-Artificial intelligence-computer-vision-computer vision problems

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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Svetlana Pavlitskaya Ş iyar Yı kmı ş J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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Semantic Segmentation for Thermal Images: A Comparative Survey

Semantic Segmentation for Thermal Images: A Comparative Surv...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zü lfiye Kü tü k Gö rkem Algan Dept. of Image Proc. &#x0026 Computer Vis. Technologies Aselsan Inc. Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.

关键词： Image segmentation computer vision Costs Semantics Neural networks Lighting Robustness

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