Results 41 to 50 of about 116,076 (264)

Remote Sensing Target Tracking in UAV Aerial Video Based on Saliency Enhanced MDnet

IEEE Access, 2019
Remote sensing target tracking in the aerial video from unmanned aerial vehicles (UAV) plays a key role in public security. As the UAV aerial video has rapid changes in scale and perspective, few pixels in the target region, and multiple similar ...
Fukun Bi, Mingyang Lei, Yanping Wang, Dan Huang   +3 more
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GreedyCenters: Satellite imagery adaptive sampling method for artificial neural networks training [PDF]

E3S Web of Conferences, 2021
The one of many significant particularities of satellite imagery is large size of images within orders of magnitude exceeds capability of modern GPGPU to train neural networks on its full size.
Gvozdev Oleg
doaj   +1 more source

Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization

CoRR, 2023
In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the concepts from the images and extend it to videos and introduce a clustering and querying approach for ...
Sumanta Bhattacharyya, Ramesh Manuvinakurike, Sahisnu Mazumder, Saurav Sahay   +3 more
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Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples

Frontiers in Plant Science, 2023
This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model
Zhenghang Zhang, Zhenghang Zhang, Lulu Ma, Lulu Ma, Chunyue Wei, Mi Yang, Mi Yang, Shizhe Qin, Shizhe Qin, Xin Lv, Xin Lv, Ze Zhang, Ze Zhang   +12 more
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An Augmented Sample Selection Framework for Prediction of Anticancer Peptides

Molecules, 2023
Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction.
Huawei Tao, Shuai Shan, Hongliang Fu, Chunhua Zhu, Boye Liu   +4 more
openaire   +3 more sources

Dynamic Adaptive Iterative Generative Adversarial Network for Hyperspectral Image Classification With Class Imbalance

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Small-sample learning improves the problem of limited labeled samples in hyperspectral image (HSI) classification to a greater extent, but still suffers from the severe problem of class imbalance, where minority classes are poorly learned and classified,
Ke Li, Ying Cui, Liguo Wang, Shan Gao, Chunhui Zhao, Tianfang Luo   +5 more
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How to choose “Good” Samples for Text Data Augmentation

, 2022
Abstract Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size.
Xiaotian Lin, Nankai Lin, Yingwen Fu, Ziyu Yang, Shengyi Jiang   +4 more
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Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most CR classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and ...
Hongjun Su, Dezhong Shi, Zhaohui Xue, Qian Du   +3 more
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Reweighting Augmented Samples by Minimizing the Maximal Expected Loss

CoRR, 2021
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the model. To address this, for the augmented samples from the same training example, we propose to assign different ...
Mingyang Yi, Lu Hou 0002, Lifeng Shang, Xin Jiang 0002, Qun Liu 0001, Zhi-Ming Ma   +5 more
openaire   +3 more sources

Discounting and Augmentation of Dispositional and Causal Attributions

Psychologica Belgica, 2006
This article investigates whether and how discounting and augmentation of dispositional and causal attributions differ between each other. In three experiments, the strength of a causal or dispositional attribution to a target actor (or object) was ...
Frank Van Overwalle
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