channel attentioncascaded feedbackcross-layer cross-channel complementsThis paper addresses the core issue of how to learn powerful features for saliency. We have two major observations. First, feature maps of
In recent years, DL methods, especially for GAN-based methods have attracted significant attention in the medical image generation field. Nevertheless, the only sigmoid cross-entropy loss function often causes unstable training in the original GAN. Although the unpaired cycleGAN owns superior performance...
The 2D self-attention-based decoder generates the text sequence according to the output of feature filter and the previously generated symbols. Extensive evaluation results show CarveNet achieves state-of-the-art on both regular and irregular scene text recognition benchmark datasets. Compared with ...
(vii) our analysis highlights the presence of biases (for example gender) in the network. Our cross-architectural comparison indicates that: (i) the pretrained models capture speaker-invariant information, and (ii)CNN modelsare competitive with Transformer models in encoding various understudied ...
channel-wise attentionsemantic segmentationsemi-supervised learningThe existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted ...
Firstly, an improved channel-wise attention mechanism is presented to propose regional attention maps and connect them to relative labels. After that, based on the assumption that objects in a semantic scene always have high-level relevance among visual and textual corpus, we further embed ...
Multi-head attentionMulti-task learningSincNet? 2022 Elsevier B.V.The expression of human emotions is a complex process that often manifests through physiological and psychological traits and results in spatio-temporal brain activity. The brain activity can be captured with an electroencephalogram (EEG...
We design the Cross-Attention Bridge Layer (CAB) to mitigate excessive feature and resolution loss when downsampling to the lowest level, ensuring meaningful information fusion during upsampling at the lowest level. Finally, we construct the Dual-Path Channel Attention (DPCA) module to guide channel...
Channel-Wise feature responsesSqueeze-and-Excitation-Residual networkHierarchical feature refinementSoftmax cross entropy lossRecently, deep learning-based saliency detection has achieved fantastic performance over conventional works. In this paper, we pay more attention to channel-wise feature responses and ...
Combined with the SE-MSCAM multi-scale attention model and the attention fusion module AFFN, the ResNet50-AFFN multi-scale channel attention fusion network is proposed. Secondly, regarding the limitation of single-scale learning of SiamRPN++ depth-wise cross correlation, the MS-DWXCorr multi-...