Long-tail learning with class descriptors
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Benchmarks
Most implemented
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Decoupling Representation and Classifier for Long-Tailed Recognition
Large-Scale Long-Tailed Recognition in an Open World
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
From Generalized zero-shot learning to long-tail with class descriptors
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Papers
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not ex…
Fine-Grained Image ClassificationImage ClassificationLong-tail LearningLong-tail learning with class descriptorsFrom Generalized zero-shot learning to long-tail with class descriptors
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompanied by side information like textual desc…
Few-Shot LearningGeneralized Few-Shot LearningGeneralized Zero-Shot LearningLong-tail Learning+2Decoupling Representation and Classifier for Long-Tailed Recognition
The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strateg…
ClassificationGeneral ClassificationLong-tail LearningLong-tail learning with class descriptors+2Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improv…
Long-tail LearningLong-tail learning with class descriptorsGeneralized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by gen…
Few-Shot LearningGeneralized Few-Shot LearningGeneralized Zero-Shot LearningGeneralized Zero-Shot Learning - Unseen+2Large-Scale Long-Tailed Recognition in an Open World
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon …
ClassificationFew-Shot LearningGeneral Classificationimbalanced classification+3