Sparse representation for robust abnormality detection in crowded scenes Pattern Recognition (2014) Z.Zhanget al. Depth-based subgraph convolutional auto-encoder for network representation learning Pattern Recognition (2019) Y.Yuanet al. Structured dictionary learning for abnormal event detection in crowded...
稀疏编码(Sparse Coding)重建的过程是从字典中自适应的选择一个或者多个字典原子,这些字典原子适合当前输入低分辨率图像块特征,最后利用这些字典原子的线性组合来得到相应的高频细节特征。需要在重建过程计算LR到HR图的原子投影矩阵,计算复杂度高。 锚点邻域回归(Anchored Neighborhood Regression, ANR)改进SC算法,SC的原子...
Sˇ roubek, "Fast convolutional sparse coding using matrix inversion lemma," Digital Signal Processing, vol. 55, pp. 44-51, Aug. 2016.M. Sˇ orel and F. Sˇ roubek. Fast convolutional sparse coding using matrix inversion lemma. Digital Signal Processing, 55:44-51, 2016. 2...
[8] An accurate and robust approach of device-free localization with convolutional autoencoder. (published in IEEE Internet of Things Journal 6.3:5825-5840, 2019). [9] Accounting for part pose estimation uncertainties during trajectory generation for part pick-up using mobile manipulators. (published...
BS4NN: binarized spiking neural networks with temporal coding and learning. Neural Process. Lett. 54 (2), 1255–1273 (2022). Article MATH Google Scholar Cire¸san, D. C., Meier, U., Gambardella, L. M. & Schmidhuber, J. Convolutional Neural Network Committees for Handwritten Character ...
which enhances the network learning ability and further improves the feature extraction ability of concealed objects in images. ResNet-50 introduces a residual module in the convolutional layer, which solves the problem of training degradation caused by the deepening of the network. The network has ...
《Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding》 《Compressing Deep Convolutional Networks using Vector Quantization》 《And the Bit Goes Down: Revisiting the Quantization of Neural Networks》 《Training with Quantization Noise for Extreme Model...
The lightweight Convolutional Neural Network MobileNetV3 with 15 bneck layers is employed as the main model in this research. Pre-trained weights from the ImageNet dataset are introduced, and the parameters of the Bneck layer are frozen. The output classes of Softmax layer are replaced with ...
Fast Convolutional Sparse Coding in the Dual Domain Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly ...
For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably ...