March 28, 2024, 4:46 a.m. | Zhizhong Huang, Mingliang Dai, Yi Zhang, Junping Zhang, Hongming Shan

cs.CV updates on arXiv.org arxiv.org

arXiv:2311.12386v3 Announce Type: replace
Abstract: Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection. Our framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (\textbf{i}) SAM to segment all possible objects as mask proposals, and (\textbf{ii}) CLIP to classify proposals to obtain …

arxiv count cs.cv framework generalized object segment type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineering Manager, Generative AI - Characters

@ Meta | Bellevue, WA | Menlo Park, CA | Seattle, WA | New York City | San Francisco, CA

Senior Operations Research Analyst / Predictive Modeler

@ LinQuest | Colorado Springs, Colorado, United States