ArcFace: Additive Angular Margin Loss for Deep Face ...

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

Explainable Artificial Intelligence (XAI)

(discriminative features of the image) with . class definitions (image-independent discriminative features of the class ) A group of people shopping at an outdoor market . There are many vegetables at the fruit stand . Generating Image Captions Generating Visual Explanations Limitations • Limited (indirect at best) explanation of internal logic

  Feature, Discriminative, Discriminative features

Learning RoI Transformer for Oriented Object Detection in ...

instances and enables to better extract discriminative features for object detection. often approached by an oriented and densely packed ob-ject detection task [37, 31, 12], which is new while well-grounded and have attracted much attention in the past decade [27, 30, 26, 18, 1]. Many of recent progress on object detection in aerial im-

  Feature, Discriminative, Discriminative features

Object Detection with Discriminatively Trained Part Based ...

a discriminative procedure that only requires bounding boxes for the objects in a set of images. The resulting system is both efficient and accurate, achieving state-of- ... [10] or bag-of-features [44]. One of the goals of our work is to address this performance gap.

  Feature, With, Part, Trained, Object, Detection, Discriminative, Object detection with discriminatively trained part, Discriminatively

SURF: Speeded Up Robust Features - ETH Z

discriminative power. Concerning the photometric deformations, we assume a simple linear model with a scale factor and offset. Notice that our detector and descriptor don’t use colour. The paper is organised as follows. Section 2 describes related work, on which our results are founded. Section 3 describes the interest point detection scheme.

  Feature, Surf, Discriminative

Verbal Behavior Milestones Assessment and Placement ...

discriminative stimuli Extinction • Reinforcement no longer happens • Behavior fades . 7/28/2016 7 Verbal Behavior ... 4 10 Imitate prosodic features: Syllable stress 5 5 Pitch, loudness, vowel duration Total: 100 EESA: Early Echoic Skills Assessment . 7/28/2016 19

  Feature, Discriminative

Abstract - arXiv

features to mutually reinforce to acquire more compact target representations, as shown in Figure2. These en-coded high-quality features further benefit the tracking model generation. • Transformer Decoder. It conveys valuable temporal information across frames. As shown in Figure2, our decoder simultaneously transfers features and spatial ...

  Feature

HOW POWERFUL ARE GRAPH NEURAL NETWORKS

the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test.

  Discriminative