To guarantee real-time location detection and improve the accuracy of mushroom segmentation, this study proposed a new spatial-channel transformer network model based on Mask-CNN (SCTMask-RCNN). The fusion of Mask-RCNN with the self-attention mechanism extracts the g...
on-line trajectory prediction is a challenging task. With the development of attention mechanism in recent years, transformer model has been applied in natural language sequence
{2024}, volume={62}, number={}, pages={1-15}, keywords={Semantics;Transformers;Decoding;Feature extraction;Task analysis;Object detection;Visualization;Convolutional neural network (CNN);cross-attention;deep learning;infrared small target detection (IRSTD);transformer}, doi={10.1109/TGRS.2024.3383649...
With the advancement of CNN and transformer technologies, lane mark detection technology is receiving increasing attention in science. The three main areas of interest of research and major achievements are as follows: 1. Tradition approaches based on vision. The primary technologies at this level ...
The main purpose of this study is to demonstrate that channel and spatial attention mechanisms optimize the transformer, which can improve the network performance. We used the overall accuracy as the evaluation criterion for this model, and all the experiments results used in the comparison were obt...
MSTSENet: Multiscale Spectral–Spatial Transformer with Squeeze and Excitation network for hyperspectral image classification Hyperspectral image (HSI) classification pertains to the task of assigning a single label to each pixel by analyzing its spectral鈥搒patial characteristics... I Ahmad,G Farooque,...
《SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification》论文:https://arxiv.org/pdf/1810.06996.pdfGitHub:https://github.com/xfanplus/Open-SCPNet 这是发表在ACCV2018上的一篇paper,做的是遮挡下的reid,即partial reid,19年2月基于pytorch的代码刚放出来。 SCP...
Mobile- former: Bridging mobilenet and transformer. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5270–5279, 2022. 1 [3] Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yan- nis Kalantidis, Marcus Rohrbach, ...
The attention module integrates seamlessly with both LSTM and Transformer frameworks. Notably, our approach reduces average computation time by 20.74% compared to the baseline model and improves performance metrics. Extensive experiments were conducted to validate the proposed framework on the Flickr30K ...
In addition, the multi-channel network multi-level structure are designed to extract richer regional features. Moreover, focal loss is introduced to balance the samples’ distribution of fine-grained image dataset. Comprehensive and comparable experiments are conducted in publicly available datasets, and...