Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

The identification of stay cable icing is crucial for robot deicing to improve efficiency and prevent damage to stay cables. Therefore, it is significant to identify the areas and degree of icing in the images of stay cables. This study proposed a two-stage model that combines U-Net and ResNet50. In the first stage, this model used U-Net to segment the surface ice and icicles from the stay cable. The image of icing obtained after segmentation was used as the input for the second stage. In the second stage, ResNet50 was used to classify the degree of icing. The experimental results show that the proposed model can successfully segment the icicles and surface ice from the stay cable icing image to complete the classification of the icing degree. The mean pixel accuracy and intersection over the union of icing were 96.65% and 82.10%, respectively. The average accuracy of the icing degree classification was 95.71%. The method proposed in this study meets the requirements of robustness, segmentation accuracy, and classification accuracy for stay cable icing recognition, which provides a research basis for the precise icing recognition of cable-deicing robots.

Section: Introductionmentioning

confidence: 99%

A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50

Yang,

Xiong,

Pei

et al. 2024

“…Chen et al [32] devised a face attention unit to capture facial structure information, while Lu et al [33] designed an external-internal split attention group to reconstruct clear facial images. Furthermore, the performance of transformers has already been proven and widely applied in computer vision tasks, such as image recognition [34,35] and image restoration [28,36,37]. The core of the transformers is a self-attention mechanism that can capture both long-and short-range correlations between words/pixels [38].…”

Section: Introductionmentioning

confidence: 99%

Feature Maps Need More Attention: A Spatial-Channel Mutual Attention-Guided Transformer Network for Face Super-Resolution

Zhang,

2024

Recently, transformer-based face super-resolution (FSR) approaches have achieved promising success in restoring degraded facial details due to their high capability for capturing both local and global dependencies. However, while existing methods focus on introducing sophisticated structures, they neglect the potential feature map information, limiting FSR performance. To circumvent this problem, we carefully design a pair of guiding blocks to dig for possible feature map information to enhance features before feeding them to transformer blocks. Relying on the guiding blocks, we propose a spatial-channel mutual attention-guided transformer network for FSR, for which the backbone architecture is a multi-scale connected encoder–decoder. Specifically, we devise a novel Spatial-Channel Mutual Attention-guided Transformer Module (SCATM), which is composed of a Spatial-Channel Mutual Attention Guiding Block (SCAGB) and a Channel-wise Multi-head Transformer Block (CMTB). SCATM on the top layer (SCATM-T) aims to promote both local facial details and global facial structures, while SCATM on the bottom layer (SCATM-B) seeks to optimize the encoded features. Considering that different scale features are complementary, we further develop a Multi-scale Feature Fusion Module (MFFM), which fuses features from different scales for better restoration performance. Quantitative and qualitative experimental results on various datasets indicate that the proposed method outperforms other state-of-the-art FSR methods.

Why Not Both? An Attention-Guided Transformer with Pixel-Related Deconvolution Network for Face Super-Resolution

Zhang,

2024

Transformer-based encoder-decoder networks for face super-resolution (FSR) have achieved promising success in delivering stunningly clear and detailed facial images by capturing local and global dependencies. However, these methods have certain limitations. Specifically, the deconvolution in upsampling layers neglects the relationship between adjacent pixels, which is crucial in facial structure reconstruction. Additionally, raw feature maps are fed to the transformer blocks directly without mining their potential feature information, resulting in suboptimal face images. To circumvent these problems, we propose an attention-guided transformer with pixel-related deconvolution network for FSR. Firstly, we devise a novel Attention-Guided Transformer Module (AGTM), which is composed of an Attention-Guiding Block (AGB) and a Channel-wise Multi-head Transformer Block (CMTB). AGTM at the top of the encoder-decoder network (AGTM-T) promotes both local facial details and global facial structures, while AGTM at the bottleneck side (AGTM-B) optimizes the encoded features. Secondly, a Pixel-Related Deconvolution (PRD) layer is specially designed to establish direct relationships among adjacent pixels in the upsampling process. Lastly, we develop a Multi-scale Feature Fusion Module (MFFM) to fuse multi-scale features for better network flexibility and reconstruction results. Quantitative and qualitative experimental results on various datasets demonstrate that the proposed method outperforms other state-of-the-art FSR methods.