Contrastive learning has emerged as a promising technique for hyperspectral image (HSI) classification. However, the inherent limitation of sliding window sampling in HSI results in partial samples within a mini-batch exhibiting extremely high similarity. Consequently, there is an increased number of negative sample pairs composed of similar samples, significantly reducing the effectiveness of contrastive learning. Moreover, prevailing classification models heavily depend on convolutional operations, emphasizing the extraction of local features but struggle to capture long-distance dependencies in both spatial and spectral dimensions. To address these problems and fully leverage the abundance of unlabeled samples, we propose a novel purified contrastive learning (PCL) framework for HSI classification. We design a complementary spatial-spectral representation encoder architecture that combines Convolutional Neural Network (CNN) and Transformer to capture local features and global dependencies. More importantly, a purified contrastive loss function is proposed based on super-pixel spatial prior. Extensive experiments on three public datasets demonstrate the superiority of PCL over state-of-the-art methods in HSI classification. The code for this work is available at https://github.com/zhaolin6/PCL for the sake of reproducibility.

Continual learning is a research field of artificial neural networks to simulate human lifelong learning ability. Although a surge of investigations has achieved considerable performance, most rely only on image modality for incremental image recognition tasks. In this paper, we propose a novel yet effective framework coined cross-modal Alternating Learning with Task-Aware representations (ALTA) to make good use of visual and linguistic modal information and achieve more effective continual learning. To do so, ALTA presents a cross-modal joint learning mechanism that leverages simultaneous learning of image and text representations to provide more effective supervision. And it mitigates forgetting by endowing task-aware representations with continual learning capability. Concurrently, considering the dilemma of stability and plasticity, ALTA proposes a cross-modal alternating learning strategy that alternately learns the task-aware cross-modal representations to match the image-text pairs between tasks better, further enhancing the ability of continual learning. We conduct extensive experiments under various popular image classification benchmarks to demonstrate that our approach achieves state-of-the-art performance. At the same time, systematic ablation studies and visualization analyses validate the effectiveness and rationality of our method. Our code will be available upon publication.

Anomaly detection (AD) has gained widespread attention due to its ability to identify defects in industrial scenarios using only normal samples. Although traditional AD methods achieved acceptable performance, they mainly focus on the current set of examples solely, leading to catastrophic forgetting of previously learned tasks when trained on a new one. Due to the limitation of flexibility and the requirements of realistic industrial scenarios, it is urgent to enhance the ability of continual adaptation of AD models. Therefore, this paper proposes a unified framework by incorporating continual learning (CL) to achieve our newly designed task of continual anomaly detection (CAD). Note that, we observe that data augmentation strategy can make AD methods well adapted to supervised CL (SCL) via constructing anomaly samples. Based on this, we hence propose a novel method named Distribution of Normal Embeddings (DNE), which utilizes the feature distribution of normal training samples from past tasks. It not only effectively alleviates catastrophic forgetting in CAD but also can be integrated with SCL methods to further improve their performance. Extensive experiments and visualization results on the popular benchmark dataset MVTec AD, have demonstrated advanced performance and the excellent continual adaption ability of our proposed method compared to other AD methods. To the best of our knowledge, we are the first to introduce and tackle the task of CAD. We believe that the proposed task and benchmark will be beneficial to the field of AD. Our code is available in thesupplementary material.