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DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

arXiv 1404.1869 ↗First posted 7 Apr 2014cs.CVcited by 66

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Abstract (arXiv)

Convolutional Neural Networks (CNNs) can provide accurate object classification. They can be extended to perform object detection by iterating over dense or selected proposed object regions. However, the runtime of such detectors scales as the total number and/or area of regions to examine per image, and training such detectors may be prohibitively slow. However, for some CNN classifier topologies, it is possible to share significant work among overlapping regions to be classified. This paper presents DenseNet, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier. Future work will involve training efficient object detectors with DenseNet feature descriptors.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2014.

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Citing papers per year: 2021: 10; 2022: 16; 2023: 14; 2024: 7; 2025: 13; 2026: 6.

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