The researchers developed a method called Feature Bias Correction to improve the accuracy of recognizing objects in datasets with imbalanced data. This method helps adjust biased features to their correct positions, making the features more significant and guiding the model to learn more accurately. By rebalancing feature importance and distinguishing similar features, the method achieved top performance in recognizing objects in long-tailed datasets.