Background: Sleep staging is important for assessing sleep architecture and supporting the diagnosis of sleep-related disorders. Manual scoring is time-consuming and subject to inter-scorer variability, while joint modeling of EEG channel relationships and frequency characteristics remains challenging. Methods: We propose DCMG-Net (dual-branch CNN and context-gated multi-view graph network), a frequency-aware network combining multi-view graph convolution with context gating for five-class sleep staging using EEG alone. Multiscale convolutions and attention extract channel features. Functional, globally learnable, and four physiological frequency-band adjacency matrices define six graph views, whose representations are adaptively fused by context gating. Results: On Sleep-EDF-20, ISRUC-S3, and the Sleep Heart Health Study (SHHS), overall accuracies were 91.2%, 90.0%, and 86.3%; macro-F1 scores were 84.4%, 88.7%, and 77.2%; and Cohen’s κ values were 0.88, 0.87, and 0.81, respectively. Macro-F1 exceeded the highest values reported by the compared literature methods by 1.9, 4.2, and 0.7 percentage points, respectively. In the ablation experiments, gating improved macro-F1 over direct concatenation by 6.1 and 2.3 percentage points on ISRUC-S3 and SHHS. Conclusions: Frequency-band topology and context gating jointly exploit EEG frequency information and channel relationships to improve multi-view classification, providing a methodological basis for portable sleep monitoring and computer-assisted clinical scoring.

Large-scale fine-grained image retrieval (FGIR) aims to retrieve images belonging to the same subcategory as a given query by capturing subtle differences in a large-scale setting. Recently, Vision Transformers (ViT) have been employed in FGIR due to their powerful self-attention mechanism for modeling long-range dependencies. However, most Transformer-based methods focus primarily on leveraging self-attention to distinguish fine-grained details, while overlooking the high computational complexity and redundant dependencies inherent to these models, limiting their scalability and effectiveness in large-scale FGIR. In this paper, we propose an Efficient and Effective ViT-based framework, termed \textbf{EET}, which integrates token pruning module with a discriminative transfer strategy to address these limitations. Specifically, we introduce a content-based token pruning scheme to enhance the efficiency of the vanilla ViT, progressively removing background or low-discriminative tokens at different stages by exploiting feature responses and self-attention mechanism. To ensure the resulting efficient ViT retains strong discriminative power, we further present a discriminative transfer strategy comprising both \textit{discriminative knowledge transfer} and \textit{discriminative region guidance}. Using a distillation paradigm, these components transfer knowledge from a larger ``teacher'' ViT to a more efficient ``student'' model, guiding the latter to focus on subtle yet crucial regions in a cost-free manner. Extensive experiments on two widely-used fine-grained datasets and four large-scale fine-grained datasets demonstrate the effectiveness of our method. Specifically, EET reduces the inference latency of ViT-Small by 42.7\% and boosts the retrieval performance of 16-bit hash codes by 5.15\% on the challenging NABirds dataset.