Weight-guided class complementing for long-tailed image recognition

•We propose a weight-guided class complementing framework to mitigate the gradient shift issue caused by unsampled classes in long-tailed scenario, including a slot-based class complementing strategy and a weight-guided feature mining loss.•We further introduce a weight refining scheme at the end of training process, which can remove the bias term in our weight-guided class complementing framework caused by long-tailed distribution.•Our plug-and-play weight-guided class complementing framework can be easily implemented to different existing approaches, achieving significant improvement on various benchmarks with new state-of-the-art performances.