Jiqing Zhang

Emotion recognition based on electroencephalogram (EEG) signals has significant potential for applications in mental health and human-computer interaction. However, existing methods often fail to fully exploit the intricate temporal dynamics and spatial dependencies inherent in EEG data. To address these challenges, we propose ST-SNN-DSFE, a novel framework that integrates temporal and spatial feature extraction to improve recognition performance. From the temporal perspective, Spiking Neural Networks (SNNs) are employed to capture fine-grained temporal dynamics, leveraging their biological plausibility and efficiency in processing temporal spikes. Additionally, the inherent characteristics of SNNs provide a slight denoising effect, which is advantageous for handling the noisy nature of EEG signals and enhancing the robustness of temporal feature extraction. From the spatial perspective, multi-scale convolutions are utilized to extract global physical information and positional encodings from EEG signals comprehensively, while prior knowledge is incorporated to define two distinct subgraph partitioning strategies based on brain regions and hemispherical divisions to extract local functional information and functional virtual positional encodings, enabling robust modeling of functional connectivity. Finally, Graph Convolutional Networks (GCNs) are employed to fuse the temporal and spatial features by integrating feature embeddings and positional relationships, improving emotion recognition performance. Extensive experiments on two benchmark datasets (SEED and SEED-IV) demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance in both subject-dependent and subject-independent scenarios.

Underwater salient object detection (USOD) faces greater challenges than general scenes due to the edge blurring which is caused by light absorption and scattering in water. Existing methods employ unrefined edge feature to perform unidirectional guidance on saliency feature, resulting in the coarse edge of saliency map. To address this issue, we propose a novel interaction-driven edge crisping network (IDENet) for underwater salient object detection. IDENet facilitates the bidi-rectional modulation of inter-features and the self-refinement of intra-feature, generates crisp saliency map and edge map. In IDENet, the interaction-driven edge guidance module (IDEGM) is designed to utilize cross-feature interaction by leveraging their correlations, facilitating saliency feature’s awareness of edge information, mitigating the interference of non-salient objects in edge feature. To learn more accurate edge region of the salient object, the edge intersection-and-union loss function (EIUL) is introduced to restrict the intersection and union of predicted saliency maps and edge maps to prevent over-expansion or under-contraction. Experimental results on two latest underwater datasets demonstrate the superiority of the proposed method over the state-of-the-art models. The source code of our method will be made available at https://github.com/ UnderwaterVisionMZTdlmu/IDENet.

Emotion recognition is essential for improving user experience and interaction quality in human-centered applications. While recent studies have leveraged both event and traditional cameras to enhance eye-based emotion recognition, their practical deployment is hindered by the scarcity of event cameras and the complexity of dual-modality frameworks. Personalization, which is critical for handling individual differences in emotional expression, is also affected by these factors, resulting in reduced performance and adaptation efficiency. To address these challenges, we propose a lightweight and personalized single-eye emotion recognition network, called LPSEER. LPSEER introduces a novel hybrid neural architecture that integrates a convolutional neural network (CNN) and a spiking neural network (SNN) to capture spatiotemporal features from video frames and events, respectively. Additionally, we design a memorybased event feature inference (MEFI) module that recalls event features from video frames, eliminating the reliance on event cameras during inference and personalization while retaining the discriminative advantages of event-based representations. Experimental results demonstrate that LPSEER achieves state-of- the-art recognition accuracy while maintaining the smallest model size and lowest computational cost. Further experiments confirm the strong generalization capabilities and the ability to achieve faster, more accurate personalization. These advantages collectively enable lightweight, accurate, and efficient emotion recognition for real-world human-centered applications.

Underwater images play a vital role in marine exploration, but are often severely degraded due to complex imaging conditions, including color distortion, haze effects, and non-uniform illumination. Existing deep learning-based enhancement methods predominantly rely on conventional RGB sensors, which struggle to distinguish between scattered and reflected light, thereby limiting enhancement performance. Polarization imaging, with its capability to capture directional light information, offers promising potential for underwater image enhancement. In this paper, we propose a lightweight yet effective polarization feature extractor that captures global spatial cues from polarization images. Additionally, we design a polarization-guided feature integration module that adaptively enhances the representational capacity of RGB features. Notably, the proposed module is plug-in and can be seamlessly integrated into existing RGB-based enhancement networks. Extensive experiments across multiple datasets demonstrate that incorporating polarization information significantly improves enhancement performance, highlighting its effectiveness as a valuable cue for underwater image enhancement. The code and pretrained models are at https://github.com/jgy0/UPGD.