Glance to count: Learning to rank with anchors for weakly-supervised crowd counting
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training gui...
محفوظ في:
المؤلفون الرئيسيون: | XIONG, Zheng, CHAI, Liangyu, LIU, Wenxi, LIU, Yongtuo, REN, Sucheng, HE, Shengfeng |
---|---|
التنسيق: | text |
اللغة: | English |
منشور في: |
Institutional Knowledge at Singapore Management University
2024
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/8533 https://ink.library.smu.edu.sg/context/sis_research/article/9536/viewcontent/GlancetoCount_av__2_.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting
بواسطة: LIU, Yongtuo, وآخرون
منشور في: (2023) -
Fine-grained domain adaptive crowd counting via point-derived segmentation
بواسطة: LIU, Yongtuo, وآخرون
منشور في: (2023) -
Crowd counting via cross-stage refinement networks
بواسطة: LIU, Yongtuo, وآخرون
منشور في: (2020) -
DEO-Net: joint density estimation and object detection for crowd counting
بواسطة: Phan, Duc Tri, وآخرون
منشور في: (2024) -
Atrous convolutions spatial pyramid network for crowd counting and density estimation
بواسطة: Ma, Junjie, وآخرون
منشور في: (2021)