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For real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for newly emerging classes. Existing efforts are carefully designed to solve such a few-shot learning problem via data augmentation, learning transferable initialization, to name a few. However, most, if not all, of them are based on a strong assumption that all the test nodes must exclusively come from novel classes, which is impractical in real-world applications. In this paper, we study a broader and more realistic problem named generalized few-shot node classification, where the test samples can be from both novel classes and base classes. Compared with the standard fewshot node classification, this new problem imposes several unique challenges, including asymmetric classification and inconsistent preference. To counter those challenges, we propose a shot-aware graph neural network (STAGER) equipped with an uncertainty-based weight assigner module for adaptive propagation. To formulate this problem from the meta-learning perspective, we propose a new training paradigm named imbalanced episodic training to ensure the label distribution is consistent between the training and test scenarios. Experiment results on four real-world datasets demonstrate the efficacy of our model, with up to 14% accuracy improvement over baselines.
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Stream-51: Streaming Classification and Novelty Detection from Videos
Deep neural networks are popular for visual perception tasks such as image classification and object detection. Once trained and deployed in a real-time environment, these models struggle to identify novel inputs not initially represented in the training distribution. Further, they cannot be easily updated on new information or they will catastrophically forget previously learned knowledge. While there has been much interest in developing models capable of overcoming forgetting, most research has focused on incrementally learning from common image classification datasets broken up into large batches. Online streaming learning is a more realistic paradigm where a model must learn one sample at a time from temporally correlated data streams. Although there are a few datasets designed specifically for this protocol, most have limitations such as few classes or poor image quality. In this work, we introduce Stream-51, a new dataset for streaming classification consisting of temporally correlated images from 51 distinct object categories and additional evaluation classes outside of the training distribution to test novelty recognition. We establish unique evaluation protocols, experimental metrics, and baselines for our dataset in the streaming paradigm.
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- Award ID(s):
- 1909696
- PAR ID:
- 10173108
- Date Published:
- Journal Name:
- CVPR Workshop on Continual Learning in Computer Vision (CLVISION)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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