Dynamicity is a critical and highly challenging aspect in Multi-Object Tracking and Segmentation (MOTS), significantly impeding the effective integration of diverse association cues. High dynamicity, such as severe occlusion or deformation, can distort appearance cues, leading to inaccurate inter-object relationships and misleading results. Conversely, in low dynamicity states, spatiotemporal consistency of appearance cues aids in recovering object states. To address this issue, we propose a straightforward, effective, and versatile Dynamicity Adaptation for Multi-object Tracking and Segmentation, named DA-Track. First, we leverage the sensitivity of appearance cues to dynamicity through pre-association, capturing dynamic behavior in objects. Second, Dynamicity Adaptation incorporates Dynamicity Selection to identify reliable appearance cues based on pre-association results and Occlusion Dynamicity Fusing to adaptively integrate appearance and motion cues by analyzing historical mask variations. Experiments on MOTS20 and KITTI MOTS datasets demonstrate DA-Track’s robust and reliable performance across diverse scenarios.

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.