Self Supervision to Distillation for Long-Tailed Visual Recognition
This work addresses the challenge of long-tailed data distribution in real-world visual recognition, which is crucial for applications like autonomous driving and surveillance, though it is incremental as it builds on existing multi-stage methods.
The paper tackles the problem of poor performance in visual recognition on long-tailed datasets by proposing a multi-stage training scheme that uses soft labels to transfer knowledge from head to tail classes, achieving state-of-the-art results with improvements of 2.7% to 4.5% over baselines on benchmarks like ImageNet-LT.
Deep learning has achieved remarkable progress for visual recognition on large-scale balanced datasets but still performs poorly on real-world long-tailed data. Previous methods often adopt class re-balanced training strategies to effectively alleviate the imbalance issue, but might be a risk of over-fitting tail classes. The recent decoupling method overcomes over-fitting issues by using a multi-stage training scheme, yet, it is still incapable of capturing tail class information in the feature learning stage. In this paper, we show that soft label can serve as a powerful solution to incorporate label correlation into a multi-stage training scheme for long-tailed recognition. The intrinsic relation between classes embodied by soft labels turns out to be helpful for long-tailed recognition by transferring knowledge from head to tail classes. Specifically, we propose a conceptually simple yet particularly effective multi-stage training scheme, termed as Self Supervised to Distillation (SSD). This scheme is composed of two parts. First, we introduce a self-distillation framework for long-tailed recognition, which can mine the label relation automatically. Second, we present a new distillation label generation module guided by self-supervision. The distilled labels integrate information from both label and data domains that can model long-tailed distribution effectively. We conduct extensive experiments and our method achieves the state-of-the-art results on three long-tailed recognition benchmarks: ImageNet-LT, CIFAR100-LT and iNaturalist 2018. Our SSD outperforms the strong LWS baseline by from $2.7\%$ to $4.5\%$ on various datasets. The code is available at https://github.com/MCG-NJU/SSD-LT.