Dense Contrastive Learning for Self-Supervised Visual Pre ...

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks

Lecture 9: CNN Architectures

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners First CNN-based winner. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 23 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners ZFNet: …

  Large, Scale, Visual, Recognition, Imagenet, Imagenet large scale visual recognition

Learning Transferable Visual Models From Natural Language ...

that predicting ImageNet-related hashtags on Instagram im-ages is an effective pre-training task. When fine-tuned to ImageNet these pre-trained models increased accuracy by over 5% and improved the overall state of the art at the time. Kolesnikov et al.(2019) andDosovitskiy et al.(2020) have also demonstrated large gains on a broader set of ...

  Large, Visual, Imagenet

ImageNet Classification with Deep Convolutional Neural ...

Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and

  With, Large, Scale, Classification, Visual, Deep, Recognition, Convolutional, Imagenet, Imagenet large scale visual recognition, Imagenet classification with deep convolutional

Classification of Trash for Recyclability Status

AlexNet [1], which won the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The architecture is relatively simple and not extremely deep, and is, of course, known to perform well. AlexNet was influential because it started a trend of CNN approaches being very popular in the Im-ageNet challenge and becoming the state of the art

  Large, Scale, Visual, Recognition, Imagenet, Agente, A meeting, Imagenet large scale visual recognition

Microsoft COCO: Common Objects in Context

Microsoft COCO: Common Objects in Context Tsung-Yi Lin 1, Michael Maire2, Serge Belongie , James Hays3, Pietro Perona2, Deva Ramanan4, Piotr Doll ar 5, C. Lawrence Zitnick 1Cornell, 2Caltech, 3Brown, 4UC Irvine, 5Microsoft Research Abstract. We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object

  Microsoft, Context, Common, Recognition, Object, Coco, Microsoft coco, Common objects in context

Video Swin Transformer

model pre-trained on a large-scale image dataset. With a model pre-trained on ImageNet-21K, we interestingly find that the learning rate of the backbone architecture needs to be smaller (e.g. 0.1 ) than that of the head, which is randomly initialized. As a …

  Large, Scale, Imagenet

Quo Vadis, Action Recognition? A New Model and the ...

ImageNet. In this paper we demonstrate that video models are best pre-trained on videos and report significant improvements by using spatio-temporal classifiers pre-trained on Kinetics, a freshly collected, large, challenging human action video dataset. mentation, depth prediction, pose estimation, action classi-fication.

  Large, Recognition, Imagenet

ImageNet: A Large-Scale Hierarchical Image Database

show that ImageNet is a large-scale, accurate and diverse image database (Section2). In Section4, we present a few simple application examples by exploiting the current Ima-geNet, mostly the mammal and vehicle subtrees. Our goal is to show that ImageNet can serve as a useful resource for visual recognition applications such as object recognition,

  Large, Scale, Visual, Recognition, Imagenet, Gentes, A meeting, Visual recognition