Enhancing Zero-shot Counting via Language-guided Exemplar Learning

• URL: http://arxiv.org/abs/2402.05394v1
• Date: Thu, 8 Feb 2024 04:07:38 GMT
• Title: Enhancing Zero-shot Counting via Language-guided Exemplar Learning
• Authors: Mingjie Wang and Jun Zhou and Yong Dai and Eric Buys and Minglun Gong
• Abstract summary: Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency. This paper proposes a novel ExpressCount to enhance zero-shot object counting by delving deeply into language-guided exemplar learning. The ExpressCount is comprised of an innovative Language-oriented Exemplar Perceptron and a downstream visual Zero-shot Counting pipeline.
• Score: 17.479926342093677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Apart from building a bridge between the LLM in vogue and the visual counting tasks, expression-guided exemplar estimation significantly advances zero-shot learning capabilities for counting instances with arbitrary classes. Moreover, devising a FSC-147-Express with annotations of meticulous linguistic expressions pioneers a new venue for developing and validating language-based counting models. Extensive experiments demonstrate the state-of-the-art performance of our ExpressCount, even showcasing the accuracy on par with partial CSC models.

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