Long-tailed recognition is one of the most challenging problems in computer vision.Long-tailed recognition has a wide range of applications in the real world, and it is of great significance to study long-tailed recognition.For long-tailed distribution data, due to the unbalanced sample size between classes and the lack of sufficient training samples for the large tail classes, it is difficult to find a clear boundary between classes during the training process. To address this issue, we combine meta pre-training and supervised contrastive learning, and propose MBCP-BB (meta balanced contrastive pre-training and batch balance), a long-tailed recognition method based on a balanced contrastive learning strategy. MBCP-BB adopts a decoupled learning method for model training: A feature extractor with excellent feature representation ability is obtained through pre-training, and in the fine-tuning stage, the feature extractor is fixed and the classifier is retrained. This method highlights the importance of feature learning, and designs a balanced contrastive learning strategy to guide the feature learning process, so that supervised contrastive learning techniques can be effectively applied to long-tailed recognition scenarios. When performing feature learning, first reduce the samples of the head classes appropriately, and use the few-shot image generation technology to generate new samples for the tail classes; then use the class prototype of each class as supplementary samples for training. In the decoupled learning training mode, the potential of the feature extractor and classifier is fully mined, and the training process of the classifier is greatly simplified while enhancing the feature learning ability of the model. A large number of experiments are carried out on several long-tailed benchmark datasets, and compared with seven representative algorithms from multiple perspectives, the experimental results show that the proposed method is superior to the compared algorithms.

Deep neural network (DNN) has strong representation learning ability, but it is vulnerable and easy to be fooled by adversarial examples. In order to handle the vulnerability of DNN, many methods have been proposed. The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples, which are generated by adding perturbations to the original images. In this paper, we propose a novel adversarial example generation method, called DCVAE-adv. Different from the existing methods, DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images. Furthermore, the proposed method can be applied to both white box and black box attacks. In addition, in the inference stage, the adversarial examples can be generated without loading the original images into memory, which greatly reduces the memory overhead. We compared DCVAE-adv with three most advanced adversarial attack algorithms: FGSM, AdvGAN, and AdvGAN++. The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack. Our code is available at https://github.com/xzforeverlove/DCVAE-adv.