Counting objects in image with few or zero examples using shape and image features with LOCA

Counting objects in image with few or zero examples using shape and image features with LOCA

A Low-Shot Object Counting Network With Iterative Prototype Adaptation
arXiv paper abstract https://arxiv.org/abs/2211.08217
arXiv PDF paper https://arxiv.org/pdf/2211.08217.pdf

… consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot).

The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts.

Existing methods extract queries by feature pooling, but neglect the shape information (e.g., size and aspect), which leads to a reduced object localization accuracy and count estimates.

… propose a Low-shot Object Counting network with iterative prototype Adaptation (LOCA) … which iteratively fuses the exemplar shape and appearance queries with image features.

The module is easily adapted to zero-shot scenario, enabling LOCA to cover the entire spectrum of low-shot counting problems.

LOCA outperforms all recent state-of-the-art methods … by 20–30% in RMSE on one-shot and few-shot and achieves state-of-the-art on zero-shot scenarios, while demonstrating better generalization capabilities.

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.