Contrastive learning facilitates the acquisition of informative skeleton representations for unsupervised action recognition by leveraging effective positive and negative sample pairs. However, most existing methods construct these pairs through weak or strong data augmentations, which typically rely on random appearance alterations of skeletons. While such augmentations are somewhat effective, they introduce semantic variations only indirectly and face two inherent limitations. First, simply modifying the appearance of skeletons often fails to reflect meaningful semantic variations. Second, random perturbations can unintentionally blur the boundary between positive and negative pairs, weakening the contrastive objective. To address these challenges, we propose an attack-driven augmentation framework that explicitly introduces semantic-level perturbations. This approach facilitates the generation of hard positives while guiding the model to mine more informative hard negatives. Building on this idea, we present Attack-Augmented Mixing-Contrastive Skeletal Representation Learning (A2MC), a novel framework that focuses on contrasting hard positive and hard negative samples for more robust representation learning. Within A2MC, we design an Attack-Augmentation (Att-Aug) module that integrates both targeted (attack-based) and untargeted (augmentation-based) perturbations to generate informative hard positive samples. In parallel, we propose the Positive-Negative Mixer (PNM), which blends hard positive and negative features to synthesize challenging hard negatives. These are then used to update a mixed memory bank for more effective contrastive learning. Comprehensive evaluations across three public benchmarks demonstrate that our approach, termed A2MC, achieves performance on par with or exceeding existing state-of-the-art methods.

Multimodal Large Language Models (MLLMs) with Federated Learning (FL) can quickly adapt to privacy-sensitive tasks, but face significant challenges such as high communication costs and increased attack risks, due to their reliance on multi-round communication. To address this, One-shot FL (OFL) has emerged, aiming to complete adaptation in a single client-server communication. However, existing adaptive ensemble OFL methods still need more than one round of communication, because correcting heterogeneity-induced local bias relies on aggregated global supervision, meaning they still do not achieve true one-shot communication. In this work, we make the first attempt to achieve true one-shot communication for MLLMs under OFL, by investigating whether implicit (i.e., initial rather than aggregated) global supervision alone can effectively correct local training bias. Our key finding from the empirical study is that imposing directional supervision on local training substantially mitigates client conflicts and local bias. Building on this insight, we propose YOCO, in which directional supervision with sign-regularized LoRA B enforces global consistency, while sparsely regularized LoRA A preserves client-specific adaptability. Experiments demonstrate that YOCO cuts communication to $\sim$0.03\% of multi-round FL while surpassing those methods in several multimodal scenarios and consistently outperforming all one-shot competitors.

Federated Instruction Tuning (FIT) advances collaborative training on decentralized data, crucially enhancing model's capability and safeguarding data privacy. However, existing FIT methods are dedicated to handling data heterogeneity across different clients (i.e., client-aware data heterogeneity), while ignoring the variation between data from different domains (i.e., domain-aware data heterogeneity). When scarce data needs supplementation from related fields, these methods lack the ability to handle domain heterogeneity in cross-domain training. This leads to domain-information catastrophic forgetting in collaborative training and therefore makes model perform sub-optimally on the individual domain. To address this issue, we introduce DoFIT, a new Domain-aware FIT framework that alleviates catastrophic forgetting through two new designs. First, to reduce interference information from the other domain, DoFIT finely aggregates overlapping weights across domains on the inter-domain server side. Second, to retain more domain information, DoFIT initializes intra-domain weights by incorporating inter-domain information into a less-conflicted parameter space. Experimental results on diverse datasets consistently demonstrate that DoFIT excels in cross-domain collaborative training and exhibits significant advantages over conventional FIT methods in alleviating catastrophic forgetting. Code is available at [this link](https://github.com/1xbq1/DoFIT).

Contrastive learning has been successfully leveraged to learn action representations for addressing the problem of semisupervised skeleton-based action recognition. However, most contrastive learning-based methods only contrast global features mixing spatiotemporal information, which confuses the spatial- and temporal-specific information reflecting different semantic at the frame level and joint level. Thus, we propose a novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework to comprehensively learn more abundant representations of skeleton-based actions by jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features. In SDS-CL, we design a new spatiotemporal-decoupling intra-inter attention (SIIA) mechanism to obtain the spatiotemporal-decoupling attentive features for capturing spatiotemporal specific information by calculating spatial- and temporal-decoupling intra-attention maps among joint/motion features, as well as spatial- and temporal-decoupling inter-attention maps between joint and motion features. Moreover, we present a new spatial-squeezing temporal-contrasting loss (STL), a new temporal-squeezing spatial-contrasting loss (TSL), and the global-contrasting loss (GL) to contrast the spatial-squeezing joint and motion features at the frame level, temporal-squeezing joint and motion features at the joint level, as well as global joint and motion features at the skeleton level. Extensive experimental results on four public datasets show that the proposed SDS-CL achieves performance gains compared with other competitive methods.

In the semi-supervised skeleton-based action recognition task, obtaining more discriminative information from both labeled and unlabeled data is a challenging problem. As the current mainstream approach, contrastive learning can learn more representations of augmented data, which can be considered as the pretext task of action recognition. However, such a method still confronts three main limitations: 1) It usually learns global-granularity features that cannot well reflect the local motion information. 2) The positive/negative pairs are usually pre-defined, some of which are ambiguous. 3) It generally measures the distance between positive/negative pairs only within the same granularity, which neglects the contrasting between the cross-granularity positive and negative pairs. Toward these limitations, we propose a novel Multi-granularity Anchor-Contrastive representation Learning (dubbed as MAC-Learning) to learn multi-granularity representations by conducting inter- and intra-granularity contrastive pretext tasks on the learnable and structural-link skeletons among three types of granularities covering local, context, and global views. To avoid the disturbance of ambiguous pairs from noisy and outlier samples, we design a more reliable Multi-granularity Anchor-Contrastive Loss (dubbed as MAC-Loss) that measures the agreement/disagreement between high-confidence soft-positive/negative pairs based on the anchor graph instead of the hard-positive/negative pairs in the conventional contrastive loss. Extensive experiments on both NTU RGB+D and Northwestern-UCLA datasets show that the proposed MAC-Learning outperforms existing competitive methods in semi-supervised skeleton-based action recognition tasks.

Semi-supervised skeleton-based action recognition is a challenging problem due to insufficient labeled data. For addressing this problem, some representative methods leverage contrastive learning to obtain more features from the pre-augmented skeleton actions. Such methods usually adopt a two-stage way: first randomly augment samples, and then learn their representations via contrastive learning. Since skeleton samples have already been randomly augmented, the representation ability of the subsequent contrastive learning is limited due to the inconsistency between the augmentations and representations. Thus, we propose a novel X-invariant Contrastive Augmentation and Representation learning (X-CAR) framework to thoroughly obtain rotate-shear-scale (X for short) invariant features by learning augmentations and representations of skeleton sequences in a one-stage way. In X-CAR, a new Adaptive-combination Augmentation (AA) mechanism is designed to rotate, shear, and scale the skeletons by learnable controlling factors in an adaptive way rather than a random way. Here, such controlling factors are also learned in the whole contrastive learning process, which can facilitate the consistency between the learned augmentations and representations of skeleton sequences. In addition, we relax the pre-definition of positive and negative samples to avoid the confusing allocation of ambiguous samples, and present a new Pull-Push Contrastive Loss (PPCL) to pull the augmenting skeleton close to the original skeleton, while push far away from the other skeletons. Experimental results on both NTU RGB+D and North-Western UCLA datasets show that the proposed X-CAR achieves better accuracy compared with other competitive methods in the semi-supervised scenario.