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Long-tailed Object Detection

The training dataset obeys a long-tailed distribution, i.e., the head categories occupy most samples whereas the tail categories only own a few samples. However, the testing dataset is balanced across categories.

Papers

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Showing 11–20 of 25 papers

TitleStatusHype
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object DetectionCode1
SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object DetectionCode1
Long-tail Detection with Effective Class-MarginsCode1
Adaptive Hierarchical Representation Learning for Long-Tailed Object Detection—0
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data—0
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios—0
Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection—0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount—0
Rectify the Regression Bias in Long-Tailed Object Detection—0
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