CGAN: closure-guided attention network for salient object detection

Das, Dibyendu; Shit, Sahadeb; Ray, Dip Narayan; Majumder, S.

doi:10.1007/s00371-021-02222-2

Cited by 15 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, emotions are not always clearly detected when the faces are sweating. However, CNN has improved recognition accuracy due to the growing calculating power, and many networks have been proposed, such as U-Net, 19 ResNet, 20 and VGG net 16 , 32 . The proposed method has three modules, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The third is the attention-based fusion module. The encoder network’s design is topologically similar to the convolutional layers of the VGG16 16 network. The decoder network’s function is to transfer the low-contrast encoder image features to the high-output quality extracted features for pixel-wise recognition.…”

Section: Methodsmentioning

confidence: 99%

“…Gabor wavelet transform, 12 Haar wavelet transform, 13 active presence model, 14 and local binary pattern (LBP) are feature extraction techniques applied on static images, whereas dynamic-based 15 – 17 methods adopt the temporal relationship in the sequence of input facial expressions within adjacent frames. The hidden Markov model, support vector machine (SVM), 17 artificial neural networks, 18 and AdaBoost 18 are widely used techniques for facial expression detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time emotion recognition using end-to-end attention-based fusion network

et al. 2023

View full text Add to dashboard Cite

.Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time emotion recognition using end-to-end attention-based fusion network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It restores the spatial resolution by upsampling the encoder image to its original size. 21 We use five decoder modules as D j , where j is the number of deconvolution layers. All of the feature maps from the preceding decoder layer are up-sampled by a factor of two using transposed convolution to generate an up-sampled feature map, which is defined as…”

Section: Encoder-decoder Module Dehaze Networkmentioning

confidence: 99%

An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

Shit

Das

Ray

et al. 2023

Computer Animation & Virtual

View full text Add to dashboard Cite

Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual‐based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network‐based image dehazing and detection approach, called end to end dehaze and detection network (EDD‐N), for proper image visualization and detection. This network is trained on real‐time hazy images that are directly used to recover dehaze images without a transmission map. EDD‐N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real‐time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real‐time object detection tasks in adverse weather conditions and improved the intelligent transportation system.

show abstract

“…To solve the problem that the features of low-resolution small targets cannot be detected, some scholars have combined the generation of confrontation networks with detection models. They proposed methods such as Perceptual GAN [28], SOD-MTGAN [29], and CGAN [30]. The complexity of generating an adversarial network is too high to meet the needs of UAV aerial image target detection.…”

Section: Introductionmentioning

confidence: 99%

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

Zhang¹,

Deng²,

Chen³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and better retain important feature information.(2) The feature information is enriched by improving the multi-scale feature pyramid structure and strengthening the feature fusion at different scales. (3) A new loss function is created and the diagonal penalty term of the anchor frame is introduced to improve the speed of training and the accuracy of reasoning. The proposed model is called residual feature fusion triple attention YOLO (RT-YOLO). Experiments showed that the mean average precision (mAP) of RT-YOLO is increased from 57.2% to 60.8% on the vehicle detection in aerial image (VEDAI) dataset, and the mAP is also increased by 1.7% on the remote sensing object detection (RSOD) dataset. The results show that the RT-YOLO outperforms other mainstream models in UAV aerial image object detection.

show abstract

CGAN: closure-guided attention network for salient object detection

Cited by 15 publications

References 59 publications

Real-time emotion recognition using end-to-end attention-based fusion network

Real-time emotion recognition using end-to-end attention-based fusion network

An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

Contact Info

Product

Resources

About