Combining diverse feature and graph representations from multiple extractors with GNNs and rank aggregation improves semi-supervised image classification accuracy.
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
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abstract
This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
Combining diverse feature and graph representations from multiple extractors with GNNs and rank aggregation improves semi-supervised image classification accuracy.