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

CS231A Course Notes 3: Epipolar Geometry

then present how this geometry can aid in further understanding the world around us. 2 Epipolar Geometry Figure 2: The general setup of epipolar geometry. The gray region is the epipolar plane. The orange line is the baseline, while the two blue lines are the epipolar lines. Often in multiple view geometry, there are interesting relationships be-

  Geometry, Epipolar, Epipolar geometry

A Multi-State Constraint Kalman Filter for Vision-aided ...

defined using the epipolar geometry, and combined with IMU measurements in an Extended Kalman Filter (EKF). In [10], [11] the epipolar geometry is employed in conjunction with a statistical motion model, while in [12] epipolar constraints are fused with the dynamical model of an airplane. The use of

  Geometry, Epipolar, Epipolar geometry

Unsupervised Monocular Depth Estimation With Left-Right ...

ploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone re-sults in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency be-

  Geometry, Epipolar, Epipolar geometry

A Taxonomy and Evaluation of Dense Two-Frame Stereo ...

calibration and epipolar geometry. This is arguably the best-understood part of stereo vision; we therefore assume in this paper that we are given a pair of rectified images as input. Recent references on stereo camera calibration and rectification include [130, 70, 131, 52, 39]. 2.2. Representation ...

  Geometry, Epipolar, Epipolar geometry

Structure-from-Motion Revisited

Epipolar geometry [26] describes the relation for a moving camera through the essential matrix E (calibrated) or the fundamental matrix F (uncalibrated), and can be extended to three views using the trifocal ten-sor [26]. If a valid transformation maps a sufficient number

  Form, Structure, Geometry, Motion, Epipolar, Epipolar geometry, Structure from motion

Epipolar Geometry and the Fundamental Matrix

Epipolar Geometry and the Fundamental Matrix The epipolar geometry is the intrinsic projective geometry between two views. It is independent of scene structure, and only depends on the cameras’ internal parameters and relative pose. The fundamental matrix Fencapsulates this intrinsic geometry. It is a 3 × 3 matrix of rank 2.

  Geometry, Epipolar, Epipolar geometry

Methods for 3D Reconstruction from Multiple Images

[Roy 99] Stereo Without Epipolar Lines: A Maximum-Flow Formulation. S Roy - International Journal of Computer Vision, 1999 [Boykov 03] Computing Geodesics and Minimal Surfaces via Graph Cuts . Yuri Boykov and Vladimir Kolmogorov. In International Conference on Computer Vision, (ICCV), vol. I, pp. 26-33, 2003.

  Epipolar