Hilbert scanning mechanismto better capture sequence-level local information. We also introduce a difference-guided dynamic contrastive locality learning strategy to enhance the patch-level self-similarity learning ability of the proposed network. Extensive experiments on four synthesized video deraining datase...
here we propose a novel ViT architecture which utilizes this advanced CNN architecture as a feature extractor for low-level CXR feature corpus, upon which Transformer is trained for downstream tasks of diagnosis by utilizing the self-attention mechanism in Transformer. ...
The input image is passed through the EfficientNet backbone, from which both low-level feature L and high-level feature H are extracted. Firstly, the low-level feature L is extracted from the first stage of the EfficientNet backbone network. L is then passed through the FEM to enhance the ...
By increasing the resolution through the high-resolution TEM (HR-TEM) technique, we could further investigate the alignment of the inorganic backbone and assess the purity of the low-n LDP phase. As shown by the central panel of Fig. 4a, the vertical features can be ascribed to the layered...
UAV image object detection based on self-attention guidance and global feature fusion 2024, Image and Vision Computing Show abstract MLSA-YOLO: a multi-level feature fusion and scale-adaptive framework for small object detection 2025, Journal of Supercomputing The YOLO Framework: A Comprehensive Review...
significantly enhancing translation performance. Dual encoder structure can be classified into two types based on the integration of contextual and sentence features: inside integration and outside integration [48,52]. Li et al. [48] explored the role of a multi-encoder in document-level MT and ...
Visual results of three low-level vision tasks. We choose three representative backbones (SwinIR, Uformer and Restormer) to verify the effectiveness of DegAE pretraining, since different architectures have their preferences in handling different tasks. to close the gap between the ...
Self-reinforcement, high reversibility mechanism of highly entangled double network structures The TDN hydrogel is not a completely uniform network37. As shown in Fig. 3a, c, the pores of the freeze-dried TDN hydrogel are unevenly distributed. This uneven pore distribution is not a random distribu...
According to the explanation of the lighting level value in Section "Illumina- tion model", when the value is 0, the scene is completely dark; and the scene is completely bright when it is set to 1. In Table 4b, when the lighting level value is 0.8, the image is in an over-exposed...
often use more conventional loss functions, which are vulnerable to CI. Second, they find that applying random oversampling with augmentation (ROS+) during evaluation mostly smooths the difference between these models, bringing the lower performers much closer to the level of the higher-performing ...