  In real-world scenarios, collected and annotated data often exhibit the
characteristics of multiple classes and long-tailed distribution. Additionally,
label noise is inevitable in large-scale annotations and hinders the
applications of learning-based models. Although many deep learning based
methods have been proposed for handling long-tailed multi-label recognition or
label noise respectively, learning with noisy labels in long-tailed multi-label
visual data has not been well-studied because of the complexity of long-tailed
distribution entangled with multi-label correlation. To tackle such a critical
yet thorny problem, this paper focuses on reducing noise based on some inherent
properties of multi-label classification and long-tailed learning under noisy
cases. In detail, we propose a Stitch-Up augmentation to synthesize a cleaner
sample, which directly reduces multi-label noise by stitching up multiple noisy
training samples. Equipped with Stitch-Up, a Heterogeneous Co-Learning
framework is further designed to leverage the inconsistency between long-tailed
and balanced distributions, yielding cleaner labels for more robust
representation learning with noisy long-tailed data. To validate our method, we
build two challenging benchmarks, named VOC-MLT-Noise and COCO-MLT-Noise,
respectively. Extensive experiments are conducted to demonstrate the
effectiveness of our proposed method. Compared to a variety of baselines, our
method achieves superior results.
