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Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

arXiv 2103.11285 ↗First posted 21 Mar 2021cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 52 references that are in the map. First posted on arXiv 21 Mar 2021.

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17 of 52 references.

References · 17

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5 more
  1. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  2. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  3. 2019Presence-Only Geographical Priors for Fine-Grained Image Classificationcited by 109
  4. 2018Imbalanced Deep Learning by Minority Class Incremental Rectificationcited by 65
  5. 2019Geo-Aware Networks for Fine-Grained Recognitioncited by 44

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