Jingchun Lian, Lingyu Liu, Yaxiong Wang, Yujiao Wu, Lianwei Wu, Li Zhu, Zhedong Zheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Traditional few-shot action recognition (FSAR) aims to address the problem of the scarcity of action videos, enabling the recognition of action categories with just a few labeled samples. It is generally believed that the samples in the meta-training phase and the meta-testing phase are all drawn from the same domain. However, in practical applications, they often come from different domains, which may lead to significant differences in the distribution of spatiotemporal features. Researchers have started to study the problem of cross-domain few-shot action recognition (CDFSAR). The current solution is to train the model by combining source domain video and unlabeled target domain video to improve the model’s generalization ability. In this paper, we follow this paradigm but make a more refined use of the unlabeled target domain videos to better extract transferable features. First, we decouple the source and target domain videos along the temporal dimension and extract the domain-irrelevant features in both the source and target domains. Second, in each episode, we calculate the centroid of the domain-irrelevant features of the target domain and perform a momentum update on this feature centroid. We use Cross-Attention to align the domain-irrelevant features of the source domain toward this dynamic centroid. Finally, we use these aligned source domain features for few-shot classification. Experimental results demonstrate that our approach significantly improves few-shot classification performance across diverse domain shifts, validating the effectiveness of our refined use of unlabeled target video. Our code has been published at the URL: https://github.com/cofly2014/MCA-TRD.git.

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, i.e., observing, comparing, and drawing, we incorporate differential image analysis into a neural oil painting model, allowing the model to effectively concentrate on the incremental impact of successive brushstrokes. To operationalize this concept, we propose the Differential Query Transformer (DQ-Transformer), a new architecture that leverages differentially derived image representations enriched with positional encoding to guide the stroke prediction process. This integration enables the model to maintain heightened sensitivity to local details, resulting in more refined and nuanced stroke generation. Furthermore, we incorporate adversarial training into our framework, enhancing the accuracy of stroke prediction and thereby improving the overall realism and fidelity of the synthesized paintings. Extensive qualitative evaluations, complemented by a controlled user study, validate that our DQ-Transformer surpasses existing methods in both visual realism and artistic authenticity, typically achieving these results with fewer strokes.

Neural oil painting synthesis is to sequentially predict brushstroke color and position, forming an oil painting step by step, which could serve as a painting teacher for education and entertainment. Existing methods usually suffer from degraded generalization for real-world photo inputs due to the training-test distribution gap, often manifesting as stroke-induced artifacts (e.g., over-smoothed textures or inconsistent granularity). In an attempt to mitigate this gap, we introduce a domain-agnostic neural painting (DANP) framework that aligns model to the test domain. In particular, we focus on updating affine parameters of normalization layers efficiently, while keeping other parameters frozen. To stabilize adaptation, our framework introduces: (1) Asymmetric Dual-Branch with mirror augmentation for robust feature alignment via geometric transformations, (2) Dual-Branch Interaction Loss combining intra-branch reconstruction and inter-branch consistency, and we also involve an empirical optimization strategy to mitigate gradient oscillations in practice. Experiments on real-world images from diverse domains (e.g., faces, landscapes, and artworks) validate the effectiveness of DANP in resolution-invariant adaptation, decreasing ~11.3% reconstruction error at 512px and ~20.3% at 1024px compared to the baseline model. It is worth noting that our method is compatible with existing methods, e.g., Paint Transformer, and further improve the ~10.3% perceptual quality. Dataset and code will be publicly released at: https://domain-agnostic-neural-oil-painting.github.io/DANP.

Surface electromyography (sEMG) signals are electrical signals released by muscles during movement, which can directly reflect the muscle conditions during various actions. When a series of continuous static actions are connected along the temporal axis, a sequential action is formed, which is more aligned with people's intuitive understanding of real-life movements. The signals acquired during sequential actions are known as sequential sEMG signals, including an additional dimension of sequence, embodying richer features compared to static sEMG signals. However, existing methods show inadequate utilization of the signals' sequential characteristics. Addressing these gaps, this paper introduces the Spatio-Temporal Feature Extraction Network (STFEN), which includes a Sequential Feature Analysis Module based on static-sequential knowledge transfer, and a Spatial Feature Analysis Module based on dynamic graph networks to analyze the internal relationships between the leads. The effectiveness of STFEN is tested on both modified publicly available datasets and on our acquired Arabic Digit Sequential Electromyography (ADSE) dataset. The results show that STFEN outperforms existing models in recognizing sequential sEMG signals. Experiments have confirmed the reliability and wide applicability of STFEN in analyzing complex muscle activities. Furthermore, this work also suggests STFEN's potential benefits in rehabilitation medicine, particularly for stroke recovery, and shows promising future applications.

The integration of EEG signals and deep learning methods is emerging as an effective approach for brain fatigue detection, particularly utilizing Graph Neural Networks(GNNs) that excel in capturing complex electrode relationships. A significant challenge within GNNs is the construction of an effective adjacency matrix that enhances spatial information learning. Concurrently, electrode aggregation in EEG has emerged as a pivotal area of research. However, conventional partitioning methods depend on task-specific prior knowledge, limiting their generalizability across diverse tasks. To Address this issue, we propose a novel mesoscopic region division approach for EEG-based driver fatigue detection, leveraging inherent data characteristics and functional connectivity-based GNN. This method adopts a two-stage approach: initially, micro-electrodes exhibiting similar functional connectivity relationships are grouped as "mesoscopic region"; subsequently, all micro-electrodes in the same group are aggregated into virtual meso-electrodes, and the fatigue state classification is subsequently based on the functional connectivity between them. Applied to a public driver fatigue detection dataset, our approach surpasses existing state-of-the-art methods in performance. Additionally, interpretive analysis provides micro and mesoscopic insights into brain regions and neuronal connections associated with alert and fatigued states.

As the resource density of cloud servers increases, cloud providers deploy hundreds of VMs concurrently on a single server, requiring a high-performance, scalable, flexible and high-density I/O virtualization method. Hardware assisted virtualization such as device pass-through with SR-IOV can achieve near-native performance, however, at the expense of flexibility and a limited device count. Traditional software-based I/O virtualization systems tend to dedicate additional computing cores for higher performance, but suffer from critical scalability problems especially in high-density cloud.