OmniCount: Multi-label Object Counting with Semantic-Geometric Priors
arxiv(2024)
摘要
Object counting is pivotal for understanding the composition of scenes.
Previously, this task was dominated by class-specific methods, which have
gradually evolved into more adaptable class-agnostic strategies. However, these
strategies come with their own set of limitations, such as the need for manual
exemplar input and multiple passes for multiple categories, resulting in
significant inefficiencies. This paper introduces a new, more practical
approach enabling simultaneous counting of multiple object categories using an
open vocabulary framework. Our solution, OmniCount, stands out by using
semantic and geometric insights from pre-trained models to count multiple
categories of objects as specified by users, all without additional training.
OmniCount distinguishes itself by generating precise object masks and
leveraging point prompts via the Segment Anything Model for efficient counting.
To evaluate OmniCount, we created the OmniCount-191 benchmark, a
first-of-its-kind dataset with multi-label object counts, including points,
bounding boxes, and VQA annotations. Our comprehensive evaluation in
OmniCount-191, alongside other leading benchmarks, demonstrates OmniCount's
exceptional performance, significantly outpacing existing solutions and
heralding a new era in object counting technology.
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