Towards On-the-Fly Novel Category Discovery in Dynamic Long-Tailed Distributions

As the diversity of real-world object categories increases, the need for sophisticated classification methods also grows. However, Novel Category Discovery (NCD), which aims to predict unseen categories, often falls short in scenarios where new categories are constantly updated and data distributions are potentially biased. In addition, existing dynamic NCD approaches assume that incremental stages introduce a fixed number of new classes and often overlook distributional biases in real-world classes. To address these limitations, we propose a novel framework, Novel Category Discovery for Dynamic Long-Tailed distribution (NCD-DLT), which deals with the more realistic and challenging scenario where imbalanced, unlabeled data are introduced incrementally and sporadically over time. Unlike conventional methods requiring k-means clustering on all test samples, our approach identifies novel categories on-the-fly, predicting categories for individual data points as they arrive. We propose an advanced hash-based clustering technique, leveraging a double-hashing strategy to mitigate collisions and incorporating a greedy hash regularization loss for sparse representations to enhance clustering capabilities. Furthermore, we implement distillation losses during training to preserve the model's discriminative power across stages without forgetting prior knowledge. Finally, we introduce a novel graph merging algorithm based on the Hash Hamming Graph, revealing the dataset's clustering structure. It serves as a mechanism for pseudo-labeling in training and acts as a post-processing tool, reallocating less confident samples to more appropriate clusters. Our comprehensive approach addresses the limitations of existing NCD methods in the dynamic scenario of novel category discovery in long-tailed distributions, demonstrating improved accuracy for both uniform and long-tailed scenarios.