  Solving long-tail large vocabulary object detection with deep learning based
models is a challenging and demanding task, which is however under-explored.In
this work, we provide the first systematic analysis on the underperformance of
state-of-the-art models in front of long-tail distribution. We find existing
detection methods are unable to model few-shot classes when the dataset is
extremely skewed, which can result in classifier imbalance in terms of
parameter magnitude. Directly adapting long-tail classification models to
detection frameworks can not solve this problem due to the intrinsic difference
between detection and classification.In this work, we propose a novel balanced
group softmax (BAGS) module for balancing the classifiers within the detection
frameworks through group-wise training. It implicitly modulates the training
process for the head and tail classes and ensures they are both sufficiently
trained, without requiring any extra sampling for the instances from the tail
classes.Extensive experiments on the very recent long-tail large vocabulary
object recognition benchmark LVIS show that our proposed BAGS significantly
improves the performance of detectors with various backbones and frameworks on
both object detection and instance segmentation. It beats all state-of-the-art
methods transferred from long-tail image classification and establishes new
state-of-the-art.Code is available at
https://github.com/FishYuLi/BalancedGroupSoftmax.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning