Strip steel Semi-supervised learning, Multi-head Self Attention, Pseudo Label Assigner, Cycle GAN feature transfer, Defect detection 1. Introduction The assessment of strip steel surface quality stands as a cru
Transformers address the inherent shortcomings of CNNs and enhance fusion methodologies by leveraging a multi-head self-attention mechanism to capture global dependencies among images (J. Chen et al., 2023; Qu et al., 2022; Vs et al., 2022). Nonetheless, existing transformer-based fusion ...
region attention learningadversarial learningfacial expression recognition (FER)The visual emotion recognition from facial expressions easily suffers barrier problems of varying brightness, head pose change, various image scales when the recognition is performed in different domains. Therefore, it is required...
These projections are then fed into a Multi-Head Cross-Attention (MHCA), which calculates the cross-attention values between Q, K, and V from different sources. The calculation formula remains consistent with the classical self-attention formula: Attention(Q,K,V)=softmax(QKTdk)V (4) where...
To enhance the feature extraction capability, we incorporate the convolution operation into the Transformer network by utilizing the Conv-Token Embedding layer and Conv-Projection within the multi-head self-attention module. To be specific, the Conv-Token Embedding operation aims to capture local spatia...
In con- trast, the Transformer [13] architecture treats an image as a series of patch sequences and uses a multi head self attention mechanism to directly extract global feature information, allow- ing for a more comprehensive analysis of features. Due to these complementary characteristics, ...
Additionally, to address the weak correlation between regression and semantic features and the lack of abilities to extract global features which limits the detection performance of voxel-based SSD, we propose a Cross-semantic Cross-dimension Multi-head Attention(CDMHA) Block to better utilize ...
Region was categorized as North, Central, East, Northeast, West and South. 2. Personal effort factors Physical activity was coded as inactivity, only moderate, only vigorous and both moderate & v igorous26. Quit tobacco consumption was coded as never consumed, currently consuming and ...
The features from image and gene modalities are then fed to the multi-head self-attention layer, followed by the multi-head cross-attention layer to capture the cross-modality features. The latent vector [Math Processing Error] is linked to the Cox regression component, which concatenates the ...
In DCA, there are 8 attention heads for multi-head self-attention on each pathway, and the feedforward layer is ResNet18. The number of DCAs is 2. The classification head consists of a single-layer fully connected network. We employ 5-fold stratified cross-validation for evaluation. The ...