SOTAVerified

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
Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object DetectionCode1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios—0
Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount—0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
Fractal Calibration for long-tailed object detectionCode0
Rectify the Regression Bias in Long-Tailed Object Detection—0
Consensus Focus for Object Detection and minority classesCode0
Learning from Rich Semantics and Coarse Locations for Long-tailed Object DetectionCode0
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