Attention mechanisms in computer vision: A survey

Guo, Minghao; Xu, Tianxing; Liu, Jiangjiang; Liu, Zheng-Ning; Jiang, Peng-Tao; Mu, Tai‐Jiang; Zhang, Song-Hai; Martin, Ralph R.; Cheng, Ming–Ming; Hu, Shi‐Min

doi:10.1007/s41095-022-0271-y

Cited by 1,010 publications

(476 citation statements)

References 152 publications

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“…Including the attention module allows the image to properly learn the spatial position information of lesions. This module imitates humans in finding significant regions in complicated situations and has applications in a variety of vision tasks [21], including image classification, target identification, image segmentation, and facial recognition. As indicated in the correlations in Figure 2, it may be split into six types based on the data domain: channel, spatial, temporal, and branching attention mechanisms, as well as channel and spatial attention and spatial and temporal attention mechanisms.…”

Section: Attention Mechanismsmentioning

confidence: 99%

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Yang

et al. 2022

Micromachines

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Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and F1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively.

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Section: Attention Mechanismsmentioning

confidence: 99%

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Yang

et al. 2022

Micromachines

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“…It is inspired by human cognitive systems that tend to selectively concentrate on the important parts as needed when processing large amounts of information. Various fields in deep learning communities such as natural language processing [69] and computer vision [70] have widely benefited from attention mechanisms in terms of model efficiency and accuracy. The attention mechanism can also be used to enhance the message passing scheme of GNNs, while also providing interpretations over the edge importance.…”

Section: Attention-enhanced Message Passingmentioning

confidence: 99%

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

Cui¹,

Dai²,

Zhu³

et al. 2022

Preprint

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Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their established performance in other fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by 1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and 2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we also host the BrainGB website at https:// brainnet.us/ with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

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“…Attention mechanism is a dynamic process that diverts attention to important features [36]. The pioneering work of visual attention is the spatial attention network called RAM [37].…”

Section: Attention Mechanismmentioning

confidence: 99%

Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

Xiang¹,

Wang²,

An³

et al. 2022

Preprint

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Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image. Experimental results on the KITTI dataset show that this architecture achieves the state-of-theart performance and our method can directly output absolute depth without post-processing. Moreover, our experiments on the SeasonDepth dataset also demonstrate the robustness of our model to multiple unseen environments.

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Attention mechanisms in computer vision: A survey

Cited by 1,010 publications

References 152 publications

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

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