Enhancing Zero-shot Counting via Language-guided Exemplar Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917585010819072 |
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| author | Wang, Mingjie Zhou, Jun Dai, Yong Buys, Eric Gong, Minglun |
| author_facet | Wang, Mingjie Zhou, Jun Dai, Yong Buys, Eric Gong, Minglun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Zero-shot Counting via Language-guided Exemplar Learning Wang, Mingjie Zhou, Jun Dai, Yong Buys, Eric Gong, Minglun Computer Vision and Pattern Recognition 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. |
| title | Enhancing Zero-shot Counting via Language-guided Exemplar Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.05394 |