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2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?Alex Kendall, Y. Gal · Neural Information Processing SystemsA Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty is presented, which makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.2015Rethinking the Inception Architecture for Computer VisionChristian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. · Computer Vision and Pattern RecognitionThis work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NASJuan R. Terven, Diana-Margarita Córdova-Esparza, J. Romero-González · Machine Learning and Knowledge ExtractionA comprehensive analysis of YOLO’s evolution is presented, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers.2022Design and Development of Cost-Effective Child Surveillance System using Computer Vision TechnologyVedavyas Peddiraju, Ramchandar Rao Pamulaparthi, Chakaradhar Adupa et al. · 2022 International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC)2021Attention mechanisms in computer vision: A surveyMeng-Hao Guo, Tianhan Xu, Jiangjiang Liu et al. · Computational Visual MediaThis survey provides a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention.2001Multiple View Geometry in Computer VisionBernhard P. Wrobel · Künstliche Intell.2025Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agricultureAbhishek Upadhyay, N. Chandel, Krishna Pratap Singh et al. · Artificial Intelligence ReviewThis study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis, and evaluates various DL architectures, providing guidance on the suitability of these models for production environments.2018Deep Learning for Computer Vision: A Brief ReviewA. Voulodimos, N. Doulamis, A. Doulamis et al. · Computational Intelligence and NeuroscienceA brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders are provided.2024A review of convolutional neural networks in computer visionXia Zhao, Limin Wang, Yufei Zhang et al. · Artificial Intelligence ReviewAn elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers are presented.2024Computer vision in smart agriculture and precision farming: Techniques and applicationsSumaira Ghazal, Arslan Munir, W. S. Qureshi · Artificial Intelligence in AgricultureA thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture is established, and the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms are outlined.2021Masked Autoencoders Are Scalable Vision LearnersKaiming He, Xinlei Chen, Saining Xie et al. · Computer Vision and Pattern RecognitionThis paper develops an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens.2021Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsZe Liu, Yutong Lin, Yue Cao et al. · IEEE International Conference on Computer VisionA hierarchical Transformer whose representation is computed with Shifted windows, which has the flexibility to model at various scales and has linear computational complexity with respect to image size and will prove beneficial for all-MLP architectures.2024Automated estimation of cementitious sorptivity via computer visionHossein Kabir, Jordan Wu, Sunav Dahal et al. · Nature Communications·International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) ChallengeThe state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.2021Deep learning-enabled medical computer visionA. Esteva, Katherine Chou, Serena Yeung et al. · npj Digital MedicineRecent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment is surveyed.2009Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf]A. Zelinsky · IEEE robotics & automation magazine2002Computer Vision: A Modern ApproachD. Forsyth, J. PonceComprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.2011Computer Vision - Algorithms and ApplicationsR. Szeliski · Texts in Computer ScienceComputer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.2021Florence: A New Foundation Model for Computer VisionLu Yuan, Dongdong Chen, Yi-Ling Chen et al. · arXiv.orgThis work introduces a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine, from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth), by incorporating universal visual-language representations from Web-scale image-text data.2010Vlfeat: an open and portable library of computer vision algorithmsA. Vedaldi, B. Fulkerson · ACM MultimediaVLFeat is an open and portable library of computer vision algorithms that includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization.2023Vision Transformers in medical computer vision - A contemplative retrospectionArshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar et al. · Engineering applications of artificial intelligenceThe intersection of vision transformers and medical images is investigated, an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision is proffered, and the pointers to possible solutions for future direction are deliberated.2023A Survey on Underwater Computer VisionSalma González-Sabbagh, A. Robles-Kelly · ACM Computing SurveysCurrent applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others are reviewed, and the current trends in the field are delve upon and the challenges and opportunities are examined.2023A study on computer vision for facial emotion recognitionZiqiang Huang, Chia-Chin Chiang, Jian-Hao Chen et al. · Scientific ReportsAnalysis shows that the features around the nose and mouth are critical facial landmarks for the neural networks, which would improve the understanding of neural networks and assist with improving computer vision accuracy.2023Computer Vision Applications in Intelligent Transportation Systems: A SurveyEsma Dilek, Murat Dener · Italian National Conference on SensorsHow computer vision techniques can help transportation systems to become smarter is shown by presenting a holistic picture of the literature on different CV applications in the ITS context by bringing together research from various sources.2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A SurveyNaveed Akhtar, Ajmal Saeed Mian · IEEE AccessThis paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision, reviewing the works that design adversarial attack, analyze the existence of such attacks and propose defenses against them.