Unsupervised Few-shot Learning (UFSL) is a practical approach to adapting knowledge learned from unlabeled data of base classes to novel classes with limited labeled data. Nevertheless, most existing UFSL methods may not learn generalizable features in latter training epochs due to the simplicity of meta-learning tasks constructed by data augmentation. To address this issue, we propose two novel components, namely Dual Episodic Sampling (DES) and Momentum Consistency Regularization (MCR) for UFSL. In the DES, two types of sampling strategies are used to construct harder training tasks with multiple augmentations to generate each pseudo-class of increased diversity. The MCR constrains the consistency of the backbone encoder with its momentum counterpart to learn better generalized features for novel classes. Experimental results on four datasets verify the superiority of our method for unsupervised few-shot image classification.

Class Incremental Learning (CIL) is a promising approach to addressing the catastrophic forgetting problem when learning for new categories. Though recent works based on dynamic architectures achieve convincing performance, data imbalance caused by limited size of memory and compression of the increasingly growing network are challenges to be solved. In this paper, we propose the novel Discriminative Gradient Adjustment (DGA) and Coupled Knowledge Distillation strategy (CKD) for these two challengs. The DGA mitigates the data imbalance problem by designing the loss function with a static global balance factor and a ground-truth-based dynamic factor. The CKD fully utilizes intermediate layers of the dual-branch models by feature-level distillation with moving-average weight updating for network compression. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate the superiority of our method for CIL.

Source-free universal domain adaptation (SFUniDA) is a challenging yet practical problem that adapts the source model to the target domain in the presence of distribution and category shifts without accessing source domain data. Most existing methods are developed based on a single-expert target model for both known- and unknown-class data training, such that the known- and unknown-class data in the target domain may not be separated well from each other. To address this issue, we propose a novel Cobllaborative Learning of Diverse Experts (CoDE) method for SFUniDA. In our method, unknown-class compatible source model training is designed to reserve space for the potential target unknown-class data. Two diverse experts are learned to better recognize the target known- and unknown-class data respectively by the specialized entropy discrimination. We improve the transferability of both experts by collaboratively correcting the possible misclassification errors with consistency and diversity learning. The final prediction with high confidence is obtained by gating the diverse experts based on soft neighbor density. Extensive experiments on four publicly available benchmarks demonstrate the superiority of our method compared to the state of the art.

Few-shot object detection is a promising approach to solving the problem of detecting novel objects with only limited annotated data for training. Most existing methods are developed based on the progress in few-shot classification, which pay little attention to improving the localization module and modelling class interrelation. To address these issues, this paper proposes two novel modules, namely Region-interactive Proposal Network (Ri-PN) and Class-interactive Feature Learning (Ci-FL), for better localization and classification performance, respectively. In the Ri-PN, regions of novel classes are interacted with base classes via graph convolution instead of background due to the stronger relevance between base and novel classes together with the guidance of supervised regions loss. On the other hand, the Ci-FL refines class-specific features in prototypical learning by attentive graph convolutional network. Experimental results on PASCAL VOC and MS COCO datasets verify the superiority of our method for few-shot object detection.

Few-shot object detection is an important but challenging task where only a few instances of novel categories are available. The widely used approach is to pretrain a detector on base classes with abundant samples and then fine-tune it for novel classes. Due to the extreme data imbalance between base and novel classes, the detection performance of novel classes degrades with the distribution bias. To overcome this limitation, we propose a distribution calibration strategy and a class discrimination regularization method for better few-shot detection. Based on theoretical analysis on decision margins of base and novel classes, the decision area of novel classes is enlarged to balance the prediction probability. On the other hand, to increase the separability of inter-class distributions, the similarity between class-specific representations is minimized. Extensive experiments on PASCAL VOC and MS COCO datasets verify the effectiveness and generalization ability of our method to improve few-shot object detection.