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Found 10 papers, 2 papers with code
Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching
However, the unconventional visual signals of these cameras pose a great challenge to the robustness of spiking neural networks.
Frequency and Scale Perspectives of Feature Extraction
Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction.
TeViS:Translating Text Synopses to Video Storyboards
In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images as the video storyboard to visualize the text synopsis.
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model.
Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons
Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time.
Efficient CNN Architecture Design Guided by Visualization
Our VGNetG-1. 0MP achieves 67. 7% top-1 accuracy with 0. 99M parameters and 69. 2% top-1 accuracy with 1. 14M parameters on ImageNet classification dataset.
Dynamic Multi-Scale Loss Optimization for Object Detection
With the continuous improvement of the performance of object detectors via advanced model architectures, imbalance problems in the training process have received more attention.
CE-FPN: Enhancing Channel Information for Object Detection
Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction.
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking
Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative for deep neural networks (DNNs) with high energy consumption.
Joint convolutional neural pyramid for depth map super-resolution
The convolutional neural pyramids extract information from large receptive fields of the depth map and guidance map, while the convolutional neural network effectively transfers useful structures of the guidance image to the depth image.
