Search Results - "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition"

Refine Results
  1. 1

    VizWiz Grand Challenge: Answering Visual Questions from Blind People by Gurari, Danna, Li, Qing, Stangl, Abigale J., Guo, Anhong, Lin, Chi, Grauman, Kristen, Luo, Jiebo, Bigham, Jeffrey P.

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial…”
    Get full text
    Conference Proceeding
  2. 2

    Deep Ordinal Regression Network for Monocular Depth Estimation by Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Tao, Dacheng

    ISSN: 1063-6919, 1063-6919
    Published: United States IEEE 01.06.2018
    “…Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant…”
    Get full text
    Conference Proceeding Journal Article
  3. 3

    Squeeze-and-Excitation Networks by Hu, Jie, Shen, Li, Sun, Gang

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information…”
    Get full text
    Conference Proceeding
  4. 4

    MobileNetV2: Inverted Residuals and Linear Bottlenecks by Sandler, Mark, Howard, Andrew, Zhu, Menglong, Zhmoginov, Andrey, Chen, Liang-Chieh

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and…”
    Get full text
    Conference Proceeding
  5. 5

    Non-local Neural Networks by Wang, Xiaolong, Girshick, Ross, Gupta, Abhinav, He, Kaiming

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations…”
    Get full text
    Conference Proceeding
  6. 6

    ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices by Zhang, Xiangyu, Zhou, Xinyu, Lin, Mengxiao, Sun, Jian

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing…”
    Get full text
    Conference Proceeding
  7. 7

    Cascade R-CNN: Delving Into High Quality Object Detection by Cai, Zhaowei, Vasconcelos, Nuno

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU…”
    Get full text
    Conference Proceeding
  8. 8

    High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs by Wang, Ting-Chun, Liu, Ming-Yu, Zhu, Jun-Yan, Tao, Andrew, Kautz, Jan, Catanzaro, Bryan

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks…”
    Get full text
    Conference Proceeding
  9. 9

    StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation by Choi, Yunjey, Choi, Minje, Kim, Munyoung, Ha, Jung-Woo, Kim, Sunghun, Choo, Jaegul

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and…”
    Get full text
    Conference Proceeding
  10. 10

    Residual Dense Network for Image Super-Resolution by Zhang, Yulun, Tian, Yapeng, Kong, Yu, Zhong, Bineng, Fu, Yun

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well…”
    Get full text
    Conference Proceeding
  11. 11

    Boosting Adversarial Attacks with Momentum by Dong, Yinpeng, Liao, Fangzhou, Pang, Tianyu, Su, Hang, Zhu, Jun, Hu, Xiaolin, Li, Jianguo

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences…”
    Get full text
    Conference Proceeding
  12. 12

    High Performance Visual Tracking with Siamese Region Proposal Network by Li, Bo, Yan, Junjie, Wu, Wei, Zhu, Zheng, Hu, Xiaolin

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on…”
    Get full text
    Conference Proceeding
  13. 13

    DOTA: A Large-Scale Dataset for Object Detection in Aerial Images by Xia, Gui-Song, Bai, Xiang, Ding, Jian, Zhu, Zhen, Belongie, Serge, Luo, Jiebo, Datcu, Mihai, Pelillo, Marcello, Zhang, Liangpei

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in…”
    Get full text
    Conference Proceeding
  14. 14

    Frustum PointNets for 3D Object Detection from RGB-D Data by Qi, Charles R., Liu, Wei, Wu, Chenxia, Su, Hao, Guibas, Leonidas J.

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…In this work, we study 3D object detection from RGBD data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often…”
    Get full text
    Conference Proceeding
  15. 15

    The Unreasonable Effectiveness of Deep Features as a Perceptual Metric by Zhang, Richard, Isola, Phillip, Efros, Alexei A., Shechtman, Eli, Wang, Oliver

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite…”
    Get full text
    Conference Proceeding
  16. 16

    Generative Image Inpainting with Contextual Attention by Yu, Jiahui, Lin, Zhe, Yang, Jimei, Shen, Xiaohui, Lu, Xin, Huang, Thomas S.

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can…”
    Get full text
    Conference Proceeding
  17. 17

    InLoc: Indoor Visual Localization with Dense Matching and View Synthesis by Taira, Hajime, Okutomi, Masatoshi, Sattler, Torsten, Cimpoi, Mircea, Pollefeys, Marc, Sivic, Josef, Pajdla, Tomas, Torii, Akihiko

    ISBN: 9781538664209, 1538664208
    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are…”
    Get full text
    Conference Proceeding
  18. 18

    Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks by Gupta, Agrim, Johnson, Justin, Fei-Fei, Li, Savarese, Silvio, Alahi, Alexandre

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate…”
    Get full text
    Conference Proceeding
  19. 19

    Maximum Classifier Discrepancy for Unsupervised Domain Adaptation by Saito, Kuniaki, Watanabe, Kohei, Ushiku, Yoshitaka, Harada, Tatsuya

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the…”
    Get full text
    Conference Proceeding
  20. 20

    Path Aggregation Network for Instance Segmentation by Liu, Shu, Qi, Lu, Qin, Haifang, Shi, Jianping, Jia, Jiaya

    ISSN: 1063-6919
    Published: IEEE 01.06.2018
    “…The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting…”
    Get full text
    Conference Proceeding