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Collaborative Image and Object Level Features for Image Colourisation

Collaborative Image and Object Level Features for Image Colo...

引用

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

作者： Pucci, Rita Micheloni, Christian Martinel, Niki Univ Udine Udine Italy

ISBN: (纸本)9781665448994

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user-interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image-level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn entity-level semantics, unless multiple models pre-trained with supervision are considered. In this work, we propose a single network, named UCapsNet, that takes into consideration the image-level features obtained through convolutions and entity-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such the convolutional and entity factors to produce a high-quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully unsupervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state-of-the-art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions. Code available at https://***/Riretta/Image_Colourisation_WiCV_2021.

关键词： Convolutional codes computer vision Semantics Collaboration Training data Feature extraction pattern recognition

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Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Group Leakage Overestimates Performance: A Case Study in Key...

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

作者： Ayotte, Blaine Banavar, Mahesh K. Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn 8 Clarkson Ave Potsdam NY 13699 USA

ISBN: (纸本)9781665448994

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.

关键词： Training Support vector machines Deep learning computer vision Machine learning algorithms Heuristic algorithms conferences

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Insights from the Future for Continual Learning

Insights from the Future for Continual Learning

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

作者： Douillard, Arthur Valle, Eduardo Ollion, Charles Robert, Thomas Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Univerty Campinas Campinas Brazil CMAP Ecole Polytech Palaiseau France Valeo Ai Paris France

ISBN: (纸本)9781665448994

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental setting, to incorporate existing information about the classes, prior to any training data. Usually, each task in a traditional continual learning setting evaluates the model on present and past classes, the latter with a limited number of training samples. Our setting adds future classes, with no training samples at all. We introduce Ghost Model, a representation-learning-based model for continual learning using ideas from zero-shot learning. A generative model of the representation space in concert with a careful adjustment of the losses allows us to exploit insights from future classes to constraint the spatial arrangement of the past and current classes. Quantitative results on the AwA2 and aP&Y datasets and detailed visualizations showcase the interest of this new setting and the method we propose to address it.(1)

关键词： Training computer vision Computational modeling conferences Training data pattern recognition Task analysis

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Dictionary-guided Scene Text recognition

Dictionary-guided Scene Text Recognition

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

作者： Nguyen Nguyen Thu Nguyen Vinh Tran Minh-Triet Tran Thanh Duc Ngo Thien Huu Nguyen Minh Hoai VinAI Res Hanoi Vietnam VNU HCM Univ Informat Technol Hanoi Vietnam VNU HCM Univ Sci Hanoi Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Univ Oregon Eugene OR 97403 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665445092

Language prior plays an important role in the way humans detect and recognize text in the wild. Current scene text recognition methods do use lexicons to improve recognition performance, but their naive approach of casting the output into a dictionary word based purely on the edit distance has many limitations. In this paper, we present a novel approach to incorporate a dictionary in both the training and inference stage of a scene text recognition system. We use the dictionary to generate a list of possible outcomes and find the one that is most compatible with the visual appearance of the text. The proposed method leads to a robust scene text recognition model, which is better at handling ambiguous cases encountered in the wild, and improves the overall performance of state-of-the-art scene text spotting frameworks. Our work suggests that incorporating language prior is a potential approach to advance scene text detection and recognition methods. Besides, we contribute VinText, a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. This dataset will serve as a challenging benchmark for measuring the applicability and robustness of scene text detection and recognition algorithms.

关键词： Training Visualization computer vision Casting Dictionaries Codes Text recognition

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

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ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction pattern recognition

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DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation f...

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

作者： Singh, Abhishek Chopra, Ayush Garza, Ethan Zhang, Emily Vepakomma, Praneeth Sharma, Vivek Raskar, Ramesh MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA

ISBN: (纸本)9781665445092

Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual's personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs & attributes and demonstrate the effectiveness of DISCO against state-of-the-art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack and defense schemes at https://***/splitlearning/InferenceBenchmark.

关键词： Deep learning Privacy computer vision Face recognition Collaboration Medical services Benchmark testing

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Unsupervised Visual Representation Learning by Tracking Patches in Video

Unsupervised Visual Representation Learning by Tracking Patc...

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

作者： Wang, Guangting Zhou, Yizhou Luo, Chong Xie, Wenxuan Zeng, Wenjun Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations that would help with video-related tasks. In the proposed pretraining framework, we cut an image patch from a given video and let it scale and move according to a pre-set trajectory. The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame. We discover that using multiple image patches simultaneously brings clear benefits. We further increase the difficulty of the game by randomly making patches invisible. Extensive experiments on mainstream benchmarks demonstrate the superior performance of CtP against other video pretraining methods. In addition, CtP-pretrained features are less sensitive to domain gaps than those trained by a supervised action recognition task. When both trained on Kinetics-400, we are pleasantly surprised to find that CtP-pretrained representation achieves much higher action classification accuracy than its fully supervised counterpart on Something-Something dataset.

关键词： Visualization computer vision Computational modeling Training data Games Trajectory pattern recognition

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Plant Disease recognition Using Different CNN Models 14

Plant Disease Recognition Using Different CNN Models

引用

14th International conference on Cloud Computing, Data Science and Engineering, Confluence 2024

作者： Gupta, Shivam Gilotra, Shrey Rathi, Shivam Choudhury, Tanupriya Kotecha, Ketan School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Graphic Era Deemed to Be University Cse Dept. Uttarakhand Dehradun248002 India Symbiosis Institute of Technology Symbiosis International University Symbiosis Centre for Applied Artificial Intelligence Pune411045 India Cse Dept. Maharashtra Pune412115 India

ISBN: (纸本)9798350344837

The research paper highlights the remarkable threats that plant diseases pose to global agricultural productivity and food security. The early detection and accurate recognition of these diseases are crucial for the effective management of plants. Deep learning models, such as CNN, Resnet34, Resnet50, and VGG16, have shown propitious results in computer vision tasks, including plant disease recognition. The study explores the effectiveness of these models using a comprehensive dataset consisting of images of healthy and diseased plants. The pretrained versions of the models are fine-tuned using transfer learning techniques with performance evaluation based on metrics like accuracy, precision, recall and f1 score. Comparative analysis also identifies each model's strengths and weaknesses in classifying plant diseases, considering factors like model architecture depth and complexity. The overall findings of this research contribute towards the development of automated plant disease recognition systems integrated into real-time monitoring systems. © 2024 ieee.

关键词： Deep learning

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Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging

Rotation Coordinate Descent for Fast Globally Optimal Rotati...

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

作者： Parra, Alvaro Chng, Shin-Fang Chin, Tat-Jun Eriksson, Anders Reid, Ian Univ Adelaide Sch Comp Sci Adelaide SA Australia Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9781665445092

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are rather slow in practice, even on rotation averaging instances of moderate size, thus developing specialised algorithms is vital. In this paper, we present a fast algorithm that achieves global optimality called rotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which solves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD directly maintains and updates all valid rotations throughout the iterations. This obviates the need to store a large dense semidefinite matrix. We mathematically prove the convergence of our algorithm and empirically show its superior efficiency over state-of-the-art global methods on a variety of problem configurations. Maintaining valid rotations also facilitates incorporating local optimisation routines for further speed-ups. Moreover, our algorithm is simple to implement(1).

关键词： computer vision Coordinate measuring machines Convex functions pattern recognition Noise measurement Velocity measurement Rotation measurement

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Gesture Control System Using computer vision Techniques 5

Gesture Control System Using Computer Vision Techniques

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5th International conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Linda, M. Mary Karuppusamy, M. Kaviya, M. Udhayakumar, C. KIT-Kalaignarkarunanidhini Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350379945

The purpose of the introduction of the gesture control technology was to close the gap between the physical and virtual worlds. Under the direction of mathematical algorithms, human interaction can converse with the digital world. Proposals and implementations of numerous algorithms and techniques have been made in an attempt to realize the objective of gesture recognition and digital world communication. Accelerometers and other devices can be used to track movements. This research work focuses on building and implementing a desktop computer using a webcam, a laptop running Windows 10, and the PyCharm program. In order to comprehend human expressions and facial expressions in response to commands, this project demonstrates technological advancements in computer vision, machine learning, and sensor technologies. This allows to easily power a variety of electronic devices, including game consoles, smart homes, smartphones and cars. With the use of gesture control systems, users can communicate with smart homes through freely interacting or easily understood motions, doing away with the need for more conventional physical controllers like remotes or Smartphone apps. This enables the user to wave hand in front of the computer or laptop to control specific functions. The main motive is to reduce the cost and increasing reliability by reducing the hardware usage. To be responsive and smooth, the system uses a mix of hardware and software, including IoT devices, machine learning algorithms, and in-depth cameras. © 2024 ieee.

关键词： Gesture recognition

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