scholarly journals 3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks

2021 ◽  
Author(s):  
Pierre-Etienne Martin ◽  
Jenny Benois-Pineau ◽  
Renaud Peteri ◽  
Julien Morlier
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Attention Mechanism ◽  
Table Tennis ◽  
Fine Grained ◽  
Spatio Temporal ◽  
3D Attention

scholarly journals Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71541-71549 ◽  
Author(s):  
Haoming Lin ◽  
Yuyang Hu ◽  
Siping Chen ◽  
Jianhua Yao ◽  
Ling Zhang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fine Grained ◽  
Cervical Cells

scholarly journals Fine-Grained Classification of Product Images Based on Convolutional Neural Networks

2018 ◽  
Vol 08 (04) ◽  
pp. 69-87
Author(s):  
Tongtong Liu ◽  
Rubing Wang ◽  
Jikang Chen ◽  
Shengliang Han ◽  
Jimin Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fine Grained

scholarly journals Fine grained sport action recognition with Twin spatio-temporal convolutional neural networks

2020 ◽  
Vol 79 (27-28) ◽  
pp. 20429-20447 ◽  
Author(s):  
Pierre-Etienne Martin ◽  
Jenny Benois-Pineau ◽  
Renaud Péteri ◽  
Julien Morlier
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Fine Grained ◽  
Spatio Temporal

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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