Search results with tag "Convos"
Deconvolutional Networks - matthewzeiler
www.matthewzeiler.comOur proposed model is similar in spirit to the Convo-lutional Networks of LeCun et al. [13], but quite different in operation. Convolutional networks are a bottom-up (a) (b) Figure 1. (a): “Tokens” from Fig. 2-4 of Vision by D. Marr [18]. These idealized local groupings are proposed as an intermediate
Lite-HRNet: A Lightweight High-Resolution Network
openaccess.thecvf.comWe find that the heavily-used pointwise (1 × 1) convo-lutions in shuffle blocks become the computational bottle-neck. We introduce a lightweight unit, conditional chan-nel weighting, to replace costly pointwise (1 × 1) convolu-tions in shuffle blocks. The complexity of channel weight-ing is linear w.r.t the number of channels and lower than
Spatial Pyramid Pooling in Deep Convolutional Networks …
tinman.cs.gsu.eduThe convo-lutional layers operate in a sliding-window manner and output feature maps which represent the spatial arrangement of the activations (Figure2). In fact, con- ... networks [3] cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses only a single window size. Multi-level pooling has been
Densely Connected Convolutional Networks
openaccess.thecvf.comembrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For
Dilated Residual Networks - CVF Open Access
openaccess.thecvf.comConvolutional networks were originally developed for classifying hand-written digits [9]. More recently, convolu-tional network architectures have evolved to classify much more complex images [8, 13, 14, 6]. Yet a central aspect of network architecture has remained largely in place. Convo-lutional networks for image classification ...
Learning Convolutional Neural Networks for Graphs
proceedings.mlr.pressFinally, feature learning components such as convo-lutional and dense layers are combined with the normalized neighborhood graphs as the CNN’s receptive fields. Figure2illustrates the PATCHY-SAN architecture which ... quence Neural Networks modify GNNs to use gated recur-rent units and to output sequences (Li et al.,2015).
Xception: Deep Learning With Depthwise Separable …
openaccess.thecvf.comules and depthwise separable convolutions are also possible: in effect, there is a discrete spectrum between regular convo-lutions and depthwise separable convolutions, parametrized by the number of independent channel-space segments used for performing spatial convolutions. A regular convolution (preceded by a 1x1 convolution), at one extreme ...
4D Spatio-Temporal ConvNets: Minkowski Convolutional ...
openaccess.thecvf.comAlso, we show that on 3D-videos, 4D spatio-temporal convo-lutional neural networks are robust to noise and outperform the 3D convolutional neural network. 1. Introduction In this work, we are interested in 3D-video perception. A 3D-video is a temporal sequence of 3D scans such as a video from a depth camera, a sequence of LIDAR scans,
An Empirical Study of Training Self-Supervised Vision ...
arxiv.orgthan interlaced with non-degenerated (i.e., non-1 1) convo-lutions.2 This largely closes the architectural gap between NLP and vision. ViT achieves compelling accuracy in su-pervised learning, especially with large-scale data and high-capacity models. Given these properties, we believe ViT is a must-study baseline for self-supervised learning ...
Optimizing FPGA-based Accelerator Design for Deep ...
cadlab.cs.ucla.edubased on traditional arti cial neural networks. The purpose of this classi er is to decide the likelihood of categories that the input (e.g. image) might belong to. ... will form the set of input feature maps for the next convo-lutional layer. The pseudo code of a convolutional layer can be written as that in Code 1. Figure 1: Graph of a ...
Aggregated Residual Transformations for Deep Neural Networks
openaccess.thecvf.comadopted technique to reduce redundancy of deep convo-lutional networks and accelerate/compress them. Ioan-nou et al. [16] present a “root”-patterned network for re-ducing computation, and branches in the root are realized by grouped convolutions. These methods [6, 18, 21, 16] have shown elegant compromise of accuracy with lower
Faster R-CNN: Towards Real-Time Object Detection ... - arXiv
arxiv.orgtion networks, we propose a training scheme that alternates between fine-tuning for the region proposal task and then fine-tuning for object detection, while keeping the proposals fixed. This scheme converges quickly and produces a unified network with convo-lutional features that are shared between both tasks.1
Densely Connected Convolutional Networks - arXiv
arxiv.orgembrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with Llayers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For