Deep Forest for Multiple Instance Learning

被引：0
|
作者：
Ren, Jie [1 ,2 ]
Hou, Bojian [1 ,2 ]
Jiang, Yuan [1 ,2 ]
机构：
[1] National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing,210023, China
[2] Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, Nanjing,210023, China
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2019年 / 56卷 / 08期
关键词：
D O I：
10.7544/issn1000-1239.2019.20190332
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：1670 / 1676
相关论文
共 50 条
  • [1] Deep Multiple Instance Learning for Landslide Mapping
    Zhang, Min
    Shi, Wenzhong
    Chen, Shanxiong
    Zhan, Zhao
    Shi, Zhicheng
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 18 (10) : 1711 - 1715
  • [2] Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
    Koriakina, Nadezhda
    Sladoje, Natasa
    Basic, Vladimir
    Lindblad, Joakim
    [J]. PLOS ONE, 2024, 19 (04):
  • [3] ATTENTION-BASED DEEP MULTIPLE INSTANCE LEARNING WITH ADAPTIVE INSTANCE SAMPLING
    Tarkhan, Aliasghar
    Trung Kien Nguyen
    Simon, Noah
    Bengtsson, Thomas
    Ocampo, Paolo
    Dai, Jian
    [J]. 2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022), 2022,
  • [4] Deep Multiple Instance Learning for Human Trajectory Identification
    Fan, Zipei
    Chen, Quanjun
    Jiang, Renhe
    Shibasaki, Ryosuke
    Song, Xuan
    Tsubouchi, Kota
    [J]. 27TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2019), 2019, : 512 - 515
  • [5] Target tracking based on multiple instance deep learning
    School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun
    130022, China
    不详
    130117, China
    不详
    130000, China
    [J]. Dianzi Yu Xinxi Xuebao, 12 (2906-2912):
  • [6] Attention-based Deep Multiple Instance Learning
    Ilse, Maximilian
    Tomczak, Jakub M.
    Welling, Max
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 80, 2018, 80
  • [7] Learning multiple instance deep representation for objects tracking
    Li, Chunyu
    Li, Gang
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2020, 71
  • [8] Multiple Instance Learning with Deep Instance Selection for Video-based Face Recognition
    Liu, Ning
    [J]. PROCEEDINGS OF THE 2016 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS AND INFORMATION TECHNOLOGY (ICMIT), 2016, 49 : 327 - 332
  • [9] Deep Feature Fusion Multiple Instance Learning for WaDang Recognition
    Wen, Chao
    Li, Zhan
    Li, Aiping
    Qu, Jian
    [J]. IEEE ACCESS, 2019, 7 : 98555 - 98564
  • [10] Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning
    Ganz, Jonathan
    Puget, Chloe
    Ammeling, Jonas
    Parlak, Eda
    Kiupel, Matti
    Bertram, Christof A.
    Breininger, Katharina
    Klopfleisch, Robert
    Aubreville, Marc
    [J]. BILDVERARBEITUNG FUR DIE MEDIZIN 2024, 2024, : 137 - 142
12345