Розширення набору даних ImageNET для мультимодального навчання з текстом та зображеннями by Дмитро Дашенков, Кирило Смеляков

“…Отриманий набір має містити: дані зображень, класи зображень, а саме 1000 класів об’єктів, поданих на фото з набору ImageNet, текстові описи окремих зображень і текстові описи класів зображень загалом. …”
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Towards Self-Conscious AI Using Deep ImageNet Models: Application for Blood Cell Classification by Mohamad Abou Ali, Fadi Dornaika, Ignacio Arganda-Carreras

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GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module by Nabila Zrira, Anwar Jimi, Mario Di Nardo, Issam Elafi, Maryam Gallab, Redouan Chahdi El Ouazzani

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Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy by Minyar Sassi Hidri, Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri

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Rethinking Domain‐Specific Pretraining by Supervised or Self‐Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold‐Start Active Learning by Han Yuan, Mingcheng Zhu, Rui Yang, Han Liu, Irene Li, Chuan Hong

“…Results First, domain‐specific foundation models failed to outperform ImageNet counterparts in six out of eight experiments on informative sample selection. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

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Image captioning using bidirectional LSTM neural network by Farnaz Hoseini, Anaram Yaghoobi Notash

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Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study by Vessela Krasteva, Todor Stoyanov, Stefan Naydenov, Ramun Schmid, Irena Jekova

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A Multi-Scale Feature Attention Image Recognition Algorithm by Xin MingYuan, Ang Ling Weay, Sellappan Palaniappan

“…Due to its ability to efficiently suppress irrelevant characteristics and accentuate pertinent ones, this technique may extract more robust multiscale features and enhance classification performance through meta-learning.In this paper, the effectiveness of the multi-scale attention network is verified on two datasets, namely, Mini-ImageNet and Tiered-ImageNet, and the accuracy of the method is 58.54% for 5-way 1shot and 74.76% for 5-way 5shot on the Mini-ImageNet dataset. …”
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Integrating deformable CNN and attention mechanism into multi-scale graph neural network for few-shot image classification by Yongmin Liu, Fengjiao Xiao, Xinying Zheng, Weihao Deng, Haizhi Ma, Xinyao Su, Lei Wu

“…This paper provides a comprehensive performance evaluation of the new model on both mini-ImageNet and tiered ImageNet datasets. Compared with the benchmark model, the classification accuracy has increased by 1.07% and 1.33% respectively; In the 5-way 5-shot task, the classification accuracy of the mini-ImageNet dataset was improved by 11.41%, 7.42%, and 5.38% compared to GNN, TPN, and dynamic models, respectively. …”
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Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology by Mateja Napravnik, Franko Hržić, Martin Urschler, Damir Miletić, Ivan Štajduhar

“…However, ImageNet-pretrained models showed competitive performance when fine-tuned on sufficient data. …”
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A Novel Mixed-Precision Quantization Approach for CNNs by Dan Wu, Yanzhi Wang, Yuqi Fei, Guowang Gao

“…Our experimental results on CIFAR-10 and ImageNet show that our proposed method confers advantages over several state-of-the-art methods.CIFAR-10 and ImageNet are two commonly used datasets in the field of computer vision for image classification tasks. …”
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Large-scale self-normalizing neural networks by Zhaodong Chen, Weiqin Zhao, Lei Deng, Yufei Ding, Qinghao Wen, Guoqi Li, Yuan Xie

“…To the best of our knowledge, this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.…”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…Additional testing on CIFAR, Flowers, and ImageNet datasets revealed improvements of 6%, 1%, and 6%, respectively. …”
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Tailored knowledge distillation with automated loss function learning. by Sheng Ran, Tao Huang, Wuyue Yang

“…For example, our LKD achieves 73.62% accuracy with the MobileNet model on ImageNet, significantly surpassing our KD baseline by 2.94%.…”
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Deep Separable Hypercomplex Networks by Nazmul Shahadat, Anthony S. Maida

“…We conduct experiments on CIFAR, SVHN, and Tiny ImageNet datasets and achieve better performance using fewer trainable parameters and FLOPS. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. …”
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Group-based siamese self-supervised learning by Zhongnian Li, Jiayu Wang, Qingcong Geng, Xinzheng Xu

“…When combined with a robust linear protocol, this group self-supervised learning model achieved competitive results in CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet-100 classification tasks. Most importantly, our model demonstrated significant convergence gains within just 30 epochs as opposed to the typical 1000 epochs required by most other self-supervised techniques.…”
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A feedforward mechanism for human-like contour integration. by Fenil R Doshi, Talia Konkle, George A Alvarez

“…We further demonstrate that fine-tuning ImageNet pretrained models uncovers other hidden human-like capacities in feed-forward networks, including uncrowding (reduced interference from distractors as the number of distractors increases), which is considered a signature of human perceptual grouping. …”
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Binary Transformer Based on the Alignment and Correction of Distribution by Kaili Wang, Mingtao Wang, Zixin Wan, Tao Shen

“…Experimental results on the CIFAR10, CIFAR100, ImageNet-1k, and TinyImageNet datasets show the effectiveness of the proposed binary optimization model, which outperforms the previous state-of-the-art binarization mechanisms while maintaining the same computational complexity.…”
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