Enhancing Zero-shot Counting via Language-guided Exemplar Learning

Understand

Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC).

• This paper proposes a novel ExpressCount to enhance zero-shot object counting by delving deeply into language-guided exemplar learning.
• Specifically, the ExpressCount is comprised of an innovative Language-oriented Exemplar Perceptron and a downstream visual Zero-shot Counting pipeline.
• Thereinto, the perceptron hammers at exploiting accurate exemplar cues from collaborative language-vision signals by inheriting rich semantic priors from the prevailing pre-trained Large Language Models (LLMs), whereas the counting pipeline excels in mining fine-grained features through dual-branch and cross-attention schemes, contributing to the high-quality similarity learning.