Multi-scale attention network
Web14 apr. 2024 · Yolox-nano and Yolov7-tiny are state-of-the-art detection models that use multi-scale information in combination with the path aggregation ... (2024). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv 1612, 3928. doi: 10.48550/arXiv.1612.03928. … Web22 mar. 2024 · We propose a real-time fire smoke detection algorithm based on multi-scale feature information and an attention mechanism. Firstly, the feature information layers …
Multi-scale attention network
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Web16 apr. 2024 · Deep neural networks have recently been employed to achieve better image segmentation results than conventional approaches. In this paper, we propose a novel deep learning architecture, a Multi-Scale Self-Attention Network (MSSA-Net), which can be trained on small datasets to explore relationships between pixels to achieve better … WebMultiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery Abstract: To integrate the complete degradation information of machinery, …
Web10 apr. 2024 · The results show that the proposed multi-scale path attention residual network can improve the feature learning ability of the multi-scale structure and achieve … WebMLFA shows a strong ability of learning multi-scale features of an object effectively and can be considered as a plug-and-play component to promote existing networks. The logical …
Web1 apr. 2024 · Inspired by the achievements of deep leaning based technologies, we propose a novel deep learning architecture named as Multi-scale Attention Convolutional Neural … WebUnderstanding the multi-scale visual informa- tion in a video is essential for Video Question Answering (VideoQA). Therefore, we propose a novel Multi-Scale Progressive Attention Net- work (MSPAN) to achieve relational reasoning between cross-scale video information.
Web28 mar. 2024 · Addressing the problem of the cost volume information in 2D convolution methods not being rich, a multi-scale cost attention stereo matching network is designed based on AANet+. The network structure is composed of feature extraction, cost construction, cost aggregation and disparity regression.
Web1 apr. 2024 · Multi-scale attention mechanism The MAM is a strategy that enhances useful feature maps and suppresses less useful ones according to the importance of each feature map generated by the multi-scale convolution. The goal of the MAM is to improve the recognition ability of a network. hiking keychain linkerWeb22 mai 2024 · Through the introduction of multi-scale and context-attention modules, MC-Net gains the ability to extract local and global semantic information around targets. To further improve the segmentation accuracy, we weight the pixels depending on whether they belong to targets. hiking john muir trail starting pointWeb6 ian. 2024 · Attention mechanism has attracted more and more attention. Multi-scale Attention Convolutional Neural Network (MACNN) [18] is an end-to-end network, which designs a multi-scale convolution module ... hiking johnson shut insWeb13 apr. 2024 · In general, GCNs have low expressive power due to their shallow structure. In this paper, to improve the expressive power of GCNs, we propose two multi-scale GCN … hiking jokesWeb10 apr. 2024 · Specifically, the multi-scale fully CNN aims to comprehensively capture pixel-level features with different kernel sizes, and a multi-head attention fusion module … hiking journalWeb13 aug. 2024 · From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the … hi king julienWeb21 sept. 2024 · The MA-Net can capture rich contextual dependencies based on the attention mechanism. We design two blocks: Position-wise Attention Block (PAB) and Multi-scale Fusion Attention Block (MFAB). The PAB is used to model the feature interdependencies in spatial dimensions, which capture the spatial dependencies … hiking kitchen sink