Represent, Compare, and Learn: A Similarity-Aware Framework for
Class-Agnostic Counting

Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting

16 March 2022

Zhiguo Cao

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Papers citing "Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting"

9 / 59 papers shown

Title
Can SAM Count Anything? An Empirical Study on SAM Counting Zhiheng Ma Xiaopeng Hong Qinnan Shangguan VLM 132 19 0 21 Apr 2023
Density Map Distillation for Incremental Object Counting Chenshen Wu Joost van de Weijer 65 2 0 11 Apr 2023
Zero-shot Object Counting Aoxiang Fan Hieu M. Le Vu Nguyen Viresh Ranjan Dimitris Samaras 98 46 0 03 Mar 2023
GCNet: Probing Self-Similarity Learning for Generalized Counting Network Mingjie Wang Yande Li Jun Zhou Graham W. Taylor Minglun Gong 98 14 0 10 Feb 2023
A Unified Object Counting Network with Object Occupation Prior Shengqin Jiang Qing Wang Fengna Cheng Yuankai Qi Qingshan Liu 99 7 0 29 Dec 2022
A Low-Shot Object Counting Network With Iterative Prototype Adaptation Nikola Djukic A. Lukežič Vitjan Zavrtanik Matej Kristan 89 50 0 15 Nov 2022
Novel 3D Scene Understanding Applications From Recurrence in a Single Image Shimian Zhang Skanda Bharadwaj Keaton Kraiger Yashasvi Asthana Kuanqi Cai R. Collins Yanxi Liu 121 1 0 14 Oct 2022
Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision Michael A. Hobley V. Prisacariu 133 42 0 20 May 2022
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron Ishan Misra Julien Mairal Priya Goyal Piotr Bojanowski Armand Joulin OCL SSL 352 4,110 0 17 Jun 2020