TransReID: Transformer-Based Object Re-Identification

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

Using Positive Student Engagement to Increase Student ...

Reid, Reid, & Peterson, 2005). Positive student engagement is not an easy term to define, yet we know it when we see it. Students are engaged when they “devote substantial time and effort to a task, when they care about the quality of their work, and when they commit themselves because the work seems to have significance beyond its

  Ride

An Analysis of Learning Styles and Learning Strategies ...

performed by the participant. Hence, language learning styles proposed by Reid (1984) was chosen as the interview guideline. The second question was “What are the learning strategies used by a successful language learner of English Education Study Program of Teacher Training and Education Faculty, Tanjungpura University in academic year

  Ride

Primitive Data Types - Carnegie Mellon School of Computer ...

Summer 2010 15-110 (Reid-Miller) 6 Variables • A variable is a name for a location in memory used to store a data value. • We use variables to save and restore values or the results of calculations. • The programmer has to tell Java what type of data will be store in the variable’s memory location. Its type cannot change.

  Data, Ride, Primitive, Primitive data

Evidence-Based Teaching Strategies for Students With EBD

Reid, & Nelson, 2003). Given the daunting challenges that teachers of ... method of teacher-led instruction (Hoff & Robinson, 2002). A wide variety of techniques fall under the peer-mediated instruction category, including peer modeling, peer monitoring, peer network strategies,

  Methods, Ride

The Minibeast Pack

This method is suitable for finding out what invertebrates live in long grass and stinging nettles. Equipment needed: a sweep net, magnifier collection pots and spoons Using a sweep net a child makes broad ... Lynne Reid Banks or Charlotte’s Web by E. B. White will spark interest.

  Methods, Ride

CHLORAMINE FACT SHEET

method of disinfection using chloramination; more than 1 in 5 Americans uses drinking water treated with chloramines. Chloramination involves the addition of anhydrous or aqueous ammonia (NH 3) before or after the addition of chlorine (HOCl) to produce monochloramine (NH 2 Cl). This reaction is as follows: NH 3 + HOCl = NH 2 Cl + H 2 O

  Methods