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Image color rendering based on frequency channel attention GAN
In recent years, channel attention mechanism has greatly improved the performance of computer vision-oriented network models. But the simple...
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ERCP-Net: a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network
Plant leaf diseases are a major cause of plant mortality, especially in crops. Timely and accurately identifying disease types and implementing...
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An attention mechanism module with spatial perception and channel information interaction
In the field of deep learning, the attention mechanism, as a technology that mimics human perception and attention processes, has made remarkable...
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Human-Object Interaction Detection with Channel Aware Attention
Human-object interaction detection (HOI) is a fundamental task in computer vision, which requires locating instances and predicting their...
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Single image super-resolution via global aware external attention and multi-scale residual channel attention network
Recently, deep convolutional neural networks (CNNs) have shown significant advantages in improving the performance of single image super-resolution...
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Leaf disease recognition based on channel information attention network
Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel...
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Adaptive attention mechanism for single channel speech enhancement
The recent development of speech enhancement methods has incorporated attention mechanisms for learning long-term speech signal dependencies. The...
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Single Image Deraining Using Residual Channel Attention Networks
Image deraining is a highly ill-posed problem. Although significant progress has been made due to the use of deep convolutional neural networks, this...
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Channel and temporal-frequency attention UNet for monaural speech enhancement
The presence of noise and reverberation significantly impedes speech clarity and intelligibility. To mitigate these effects, numerous deep...
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An efficient multi-scale channel attention network for person re-identification
At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient...
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Fake news detection based on dual-channel graph convolutional attention network
Fake news detection has attracted significant attention since the spread of fake news on social media has affected the media’s credibility. Some...
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Combining channel-wise joint attention and temporal attention in graph convolutional networks for skeleton-based action recognition
Graph convolutional networks (GCNs) have been shown to be effective in performing skeleton-based action recognition, as graph topology has advantages...
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Wide Activation Fourier Channel Attention Network for Super-Resolution
Attention mechanisms, especially channel attention, have been widely used in a wide range of tasks in computer vision. More recently, researchers...
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Image denoising using channel attention residual enhanced Swin Transformer
Transformers have achieved remarkable results in high-level vision tasks, but their application in low-level computer vision tasks such as image...
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High-frequency channel attention and contrastive learning for image super-resolution
Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general,...
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Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism
This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic (UWA) communication systems using the...
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Multi-Frame Cross-Channel Attention and Speaker Diarization Based Speaker-Attributed Automatic Speech Recognition System for Multi-Channel Multi-Party Meeting Transcription
This paper describes a speaker-attributed automatic speech recognition (SA-ASR) system submitted to the multi-channel multi-party meeting...
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Sensor Spoofing Detection On Autonomous Vehicle Using Channel-spatial-temporal Attention Based Autoencoder Network
Autonomous vehicles heavily rely on various sensors to evaluate their surroundings and issue essential control commands. Nonetheless, these sensors...
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Deep recurrent residual channel attention network for single image super-resolution
The models based on convolutional neural network have achieved excellent results in image super-resolution by acquiring prior knowledge from a large...
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Time-domain adaptive attention network for single-channel speech separation
Recent years have witnessed a great progress in single-channel speech separation by applying self-attention based networks. Despite the excellent...
