Deformable convolutional sampling points adaptive offset. (a) Standard convolution sampling point; (b) Deformable convolutional sampling point adaptive offset.</p
Received (to be inserted by publisher) Singular Value Decomposition (SVD) is a technique based on li...
We present a modified version of adaptive digital backpropagation based on EVM metric, and numerical...
This paper presents a new binary shape sampling and coding method. Within the framework of MPEG-4 st...
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
The left side is the feature extraction branch, and the right side is the offset learning branch. Th...
Base network is truncated from a standard network. The detection layer computes confident scores for...
project website: https://github.com/HuguesTHOMAS/KPConvInternational audienceWe present Kernel Point...
The layers from Conv4-3 to Conv9-2, which are used as the input to the detections, are denoted as th...
In this paper, a CNN weight boundary quantization strategy that is reasonable for FPGA has been plan...
Network structure diagram of the upsampling module based on subpixel convolution and dilated convolu...
Group A is the conventional distribution, Group B is the distribution after arbitrary migration, Gro...
Modification of the (UPOLS) convolution algorithm of F. Wefers [70] for moving sources.</p
International audienceConvolution Neural Networks (CNN) make breakthrough progress in many areas rec...
Temporal Convolutional Networks (TCNs) involving mono channels as input, have shown superior perform...
In this paper unequal error-correcting capabilities of convolutional codes are studied. State-transi...
Received (to be inserted by publisher) Singular Value Decomposition (SVD) is a technique based on li...
We present a modified version of adaptive digital backpropagation based on EVM metric, and numerical...
This paper presents a new binary shape sampling and coding method. Within the framework of MPEG-4 st...
The left, middle, and right sides represent standard convolution, deformable convolution, and T-defo...
The left side is the feature extraction branch, and the right side is the offset learning branch. Th...
Base network is truncated from a standard network. The detection layer computes confident scores for...
project website: https://github.com/HuguesTHOMAS/KPConvInternational audienceWe present Kernel Point...
The layers from Conv4-3 to Conv9-2, which are used as the input to the detections, are denoted as th...
In this paper, a CNN weight boundary quantization strategy that is reasonable for FPGA has been plan...
Network structure diagram of the upsampling module based on subpixel convolution and dilated convolu...
Group A is the conventional distribution, Group B is the distribution after arbitrary migration, Gro...
Modification of the (UPOLS) convolution algorithm of F. Wefers [70] for moving sources.</p
International audienceConvolution Neural Networks (CNN) make breakthrough progress in many areas rec...
Temporal Convolutional Networks (TCNs) involving mono channels as input, have shown superior perform...
In this paper unequal error-correcting capabilities of convolutional codes are studied. State-transi...
Received (to be inserted by publisher) Singular Value Decomposition (SVD) is a technique based on li...
We present a modified version of adaptive digital backpropagation based on EVM metric, and numerical...
This paper presents a new binary shape sampling and coding method. Within the framework of MPEG-4 st...
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