CSA-Net: Channel-wise Spatially Autocorrelated Attention Networks

• URL: http://arxiv.org/abs/2405.05755v2
• Date: Mon, 13 May 2024 08:17:13 GMT
• Title: CSA-Net: Channel-wise Spatially Autocorrelated Attention Networks
• Authors: Nick Nikzad, Yongsheng Gao, Jun Zhou,
• Abstract summary: We present a channel-wise spatially autocorrelated (CSA) attention mechanism for deep CNNs. Inspired by geographical analysis, the proposed CSA exploits the spatial relationships between channels of feature maps to produce an effective channel descriptor. We validate the effectiveness of the proposed CSA networks through extensive experiments and analysis on ImageNet, and MS COCO benchmark datasets.
• Score: 19.468704622654357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, convolutional neural networks (CNNs) with channel-wise feature refining mechanisms have brought noticeable benefits to modelling channel dependencies. However, current attention paradigms fail to infer an optimal channel descriptor capable of simultaneously exploiting statistical and spatial relationships among feature maps. In this paper, to overcome this shortcoming, we present a novel channel-wise spatially autocorrelated (CSA) attention mechanism. Inspired by geographical analysis, the proposed CSA exploits the spatial relationships between channels of feature maps to produce an effective channel descriptor. To the best of our knowledge, this is the f irst time that the concept of geographical spatial analysis is utilized in deep CNNs. The proposed CSA imposes negligible learning parameters and light computational overhead to the deep model, making it a powerful yet efficient attention module of choice. We validate the effectiveness of the proposed CSA networks (CSA-Nets) through extensive experiments and analysis on ImageNet, and MS COCO benchmark datasets for image classification, object detection, and instance segmentation. The experimental results demonstrate that CSA-Nets are able to consistently achieve competitive performance and superior generalization than several state-of-the-art attention-based CNNs over different benchmark tasks and datasets.

Related papers

• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [94.09442506816724]
  Blind-spot networks (BSN) have been prevalent network architectures in self-supervised image denoising (SSID) We present a transformer-based blind-spot network (TBSN) by analyzing and redesigning the transformer operators that meet the blind-spot requirement. For spatial self-attention, an elaborate mask is applied to the attention matrix to restrict its receptive field, thus mimicking the dilated convolution. For channel self-attention, we observe that it may leak the blind-spot information when the channel number is greater than spatial size in the deep layers of multi-scale architectures.
  arXiv  Detail & Related papers  (2024-04-11T15:39:10Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization [61.71504948770445]
  We propose a novel channel pruning method via Class-Aware Trace Ratio Optimization (CATRO) to reduce the computational burden and accelerate the model inference. We show that CATRO achieves higher accuracy with similar cost or lower cost with similar accuracy than other state-of-the-art channel pruning algorithms. Because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.
  arXiv  Detail & Related papers  (2021-10-21T06:26:31Z)
• Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation [36.479195100553085]
  Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels. We present channel-wise DeepNNK, a novel generalization estimate based on non-dimensional kernel regression (NNK) graphs.
  arXiv  Detail & Related papers  (2021-07-27T17:33:30Z)
• Channelized Axial Attention for Semantic Segmentation [70.14921019774793]
  We propose the Channelized Axial Attention (CAA) to seamlessly integratechannel attention and axial attention with reduced computationalcomplexity. Our CAA not onlyrequires much less computation resources compared with otherdual attention models such as DANet, but also outperforms the state-of-the-art ResNet-101-based segmentation models on alltested datasets.
  arXiv  Detail & Related papers  (2021-01-19T03:08:03Z)
• Convolutional Neural Network optimization via Channel Reassessment Attention module [19.566271646280978]
  We propose a novel network optimization module called Channel Reassessment (CRA) module. CRA module uses channel attentions with spatial information of feature maps to enhance representational power of networks. Experiments on ImageNet and MS datasets demonstrate that embedding CRA module on various networks effectively improves the performance under different evaluation standards.
  arXiv  Detail & Related papers  (2020-10-12T11:27:17Z)
• Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos [85.6430597108455]
  We propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos. It captures the common salient foreground regions among video frames and explores the spatial-temporal long-range context interdependency from such regions. Multiple spatialtemporal interaction modules within CSTNet are proposed, which exploit the spatial and temporal long-range context interdependencies on such features and spatial-temporal information correlation.
  arXiv  Detail & Related papers  (2020-04-10T10:23:58Z)
• Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images [24.35779077001839]
  We propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations. We introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism.
  arXiv  Detail & Related papers  (2020-01-09T07:47:51Z)
• Cross-scale Attention Model for Acoustic Event Classification [45.15898265162008]
  We propose a cross-scale attention (CSA) model, which explicitly integrates features from different scales to form the final representation. We show that the proposed CSA model can effectively improve the performance of current state-of-the-art deep learning algorithms.
  arXiv  Detail & Related papers  (2019-12-27T07:28:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.