Sample-specific and Context-aware Augmentation for Long Tail Image Classification
Recent long-tail classification methods generally adopt the two-stage pipeline and focus on learning the classifier to tackle the imbalanced data in the second stage via re-sampling or re-weighting, but the classifier is easily prone to overconfidence in head classes. Data augmentation is a natural way to tackle this issue. Existing augmentation methods either perform low-level transformations or apply the same semantic transformation for all samples. However, meaningful augmentations for different samples should be different. In this paper, we propose a novel sample-specific and context-aware augmentation learning method for long-tail image classification. We model the semantic within-class transformation range for each sample by a specific Gaussian distribution and design a semantic transformation generator (STG) to predict the distribution from the sample itself. To encode the context information accurately, STG is equipped with a memory-based structure. We train STG by constructing ground-truth distributions for samples of head classes in the feature space. We apply STG to samples of tail classes for augmentation in the classifier-tuning stage. Extensive experiments on four imbalanced datasets show the effectiveness of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Data Augmentationimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
On-the-Fly Data Augmentation via Gradient-Guided and Sample-Aware Influence Estimation
Data augmentation has been widely employed to improve the generalization of deep neural networks. Most existing methods apply fixed or random transformations. However, we find that sample difficulty evolves along with th…
Data AugmentationCrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware ge…
Crack SegmentationData AugmentationContext-Aware Data Augmentation for LIDAR 3D Object Detection
For 3D object detection, labeling lidar point cloud is difficult, so data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-sample effectively …
3D Object DetectionData AugmentationObjectobject-detection+1Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation
Data augmentation for deep learning benefits model training, image transformation, medical imaging analysis and many other fields. Many existing methods generate new samples from a parametric distribution, like the Gauss…
Data AugmentationImage GenerationLaPA$^2$: Length-Aware Prefix and Prompt Attention Augmentation for Long-Form Controllable Text Generation
Prefix-based methods have emerged as a promising paradigm for Controllable Text Generation (CTG) due to their parameter efficiency. However, while effective in short sequences, their controllability tends to diminish as …
Text Generation