Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the positive-negative imbalance problem, which manifests at both the label and loss levels because of the task-level partial label issue. The imbalance at the label level arises from the substantial absence of negative labels, while the imbalance at the loss level stems from the asymmetric contributions of the positive and negative loss parts to the optimization. To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR). AKD is proposed to rebalance at the loss level by emphasizing the negative label learning in classification loss and down-weighting the contribution of overconfident predictions in distillation loss. OR is designed for label rebalance, which restores the original class distribution in memory by online relabeling the missing classes. Our comprehensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate that this rebalancing strategy significantly improves performance, achieving new state-of-the-art results even with a vanilla CNN backbone.

Semantic segmentation of high-resolution remote sensing images poses challenges such as scale variability, diverse objects, and obstruction by surface elements. These factors often lead existing methods to suffer from issues like missed and false detections, as well as coarse segmentation boundaries. To tackle these challenges, this article proposes a CNN-transformer complementary and fusion network, termed as CTCFNet. It aims to enhance segmentation accuracy and robustness by extracting and integrating the complementary global and local information from high-resolution remote sensing images. The CTCFNet operates through two primary stages: feature extraction and fusion. In the feature extraction stage, a feature extractor employs convolutional neural network (CNN) and pyramid vision transformer (PVT) blocks to extract both local and global features. A boundary loss is also proposed to improve the segmentation performance for object textures and boundaries. In the feature fusion stage, a feature aggregation module (FAM) is first designed to effectively fuse local and global features at the same scale, facilitating the feature extractor to obtain more comprehensive representations. On this basis, a bi-directional decoder (BiDecoder) reconstructs multiscale features through both top-down and bottom-up directions, resulting in more precise segmentation outputs. Experiments on several high-resolution remote sensing image datasets demonstrate that the proposed method outperforms the state-of-the-art methods in terms of segmentation accuracy and generalization. The code is available athttps://github.com/ChenLu0000/CTCFNet.

Multi-Label Continual Learning (MLCL) is a framework designed for class-incremental multi-label image recognition. However, MLCL faces two critical challenges: the construction of label relationships onpast-missing and future-missing partial labelsof training data, and the problem ofcatastrophic forgetting, which leads to poor generalization. To address these challenges, this study proposes an enhanced version of the Augmented Graph Convolutional Network (AGCN++), capable of constructing cross-task label relationships and mitigating catastrophic forgetting. First, an Augmented Correlation Matrix (ACM) is constructed across all observed classes, incorporating intra-task relationships derived from hard label statistics. Additionally, inter-task relationships are established by leveraging both hard and soft labels obtained from the data, as well as a constructed expert network. Next, a novel partial label encoder (PLE) is introduced for MLCL, enabling the extraction of dynamic class representations for each partial label image as graph nodes. This PLE also facilitates the generation of soft labels, which contribute to the creation of a more persuasive ACM and effectively mitigate forgetting. Lastly, a relationship-preserving constrainter is proposed to address the issue of forgetting label dependencies across old tasks. In the AGCN++, the label relationships topology can be augmented automatically, thereby generating efficient class representations. The effectiveness of the proposed method is evaluated using two multi-label image benchmarks. The experimental results demonstrate that the proposed approach is highly effective in the context of MLCL image recognition. It can establish compelling correlations across tasks, even in scenarios where the old task labels are missing.

The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multilabel image recognition data stream. However, training on the data with different Partial Labels may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN)to build an Augmented Correlation Matrix (ACM) across the sequential partial-label tasks and sustain the catastrophic forgetting. First, in ACM, the intra-task relations derive from the hard label statistics, while the inter-task relations further leverage the soft labels from a stored expert network. Then, based on the ACM, AGCN captures label dependencies with dynamic augmented structure and yields effective class representations. Our method is evaluated on two multi-label image benchmarks and the results show that the proposed method is effective for LML image recognition.