Graph Transformers have demonstrated outstanding performance across various graph-based applications. Despite their success, applying them to large-scale graphs presents significant scalability challenges, limiting their practical use in industrial environments. Recent studies have attempted to overcome this challenge by focusing on the spatial domain of graphs, leading to the development of various scalable models. However, these approaches neglect the spectral characteristics of graphs, which are crucial for adaptively extracting information from full-frequency bands based on the graph’s inherent properties. As a result, existing scalable Graph Transformers tend to rely heavily on low-frequency features, overlooking valuable mid- and high-frequency information. This article proposes the Graph Filter Transformer (GFformer), a framework designed to effectively extract full-frequency information from large-scale graphs. Unlike existing Graph Transformers, GFformer integrates graph filters into the Transformer architecture, thereby enhancing its ability to model both structural and frequency-related properties. Utilizing the proposed Spectral Token Converter (ST-converter), GFformer generates a unique spectral token sequence for each node by incorporating features from diverse frequencies that act as tokens. This design enables the independent learning of node representations in parallel and supports mini-batch training with flexible batch sizes, making GFformer highly scalable. ST-converter employs spectral graph filters, including low-, mid-, and high-pass filters, to extract features serving as tokens. Consequently, each sequence encompasses features from various frequencies, enabling GFformer to capture comprehensive frequency information effectively. Extensive experiments on datasets of varying scales, including both homophilic and heterophilic graphs, consistently demonstrate that GFformer outperforms existing representative methods.

Zero-shot learning (ZSL) aims to recognize images from unseen (novel) classes with the training images from seen classes. The attributes of each class is exploited as auxiliary semantic information. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the seen classes to the unseen classes. However, few works study whether the auxiliary semantic information in the class-level is extensive enough or not for the ZSL task. To tackle such problem, we propose a hierarchical coupled dictionary learning (HCDL) approach to hierarchically align the visual-semantic structures in both the class-level and the image-level. Firstly, the class-level coupled dictionary is trained to establish a basic connection between visual space and semantic space. Then, the image attributes are generated based on the basic connection. Finally, the fine-grained information can be embedded by training the image-level coupled dictionary. Zero-shot recognition is performed in multiple spaces by searching the nearest neighbor class of the unseen image. Experiments on two widely used benchmark datasets show the effectiveness of the proposed approach.

Existing non-rigid shape matching methods mainly involve two disadvantages. (a) Local details and global features of shapes can not be carefully explored. (b) A satisfactory trade-off between the matching accuracy and computational efficiency can be hardly achieved. To address these issues, we propose a local-global commutative preserving functional map (LGCP) for shape correspondence. The core of LGCP involves an intra-segment geometric submodel and a local-global commutative preserving submodel, which accomplishes the segment-to-segment matching and the point-to-point matching tasks, respectively. The first submodel consists of an ICP similarity term and two geometric similarity terms which guarantee the correct correspondence of segments of two shapes, while the second submodel guarantees the bijectivity of the correspondence on both the shape level and the segment level. Experimental results on both segment-to-segment matching and point-to-point matching show that, LGCP not only generate quite accurate matching results, but also exhibit a satisfactory portability and a high efficiency.

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4 2 , and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.

In this paper, we propose angle-normal images to reduce the number of normal component channels from three to two and present predicting the angle-normal images by the reconstructed geometry images. We implement the scheme on realistic meshes. Experimental results verify effectiveness of the proposed scheme. For geometry image codec, the proposed scheme outperforms up 1.78 dB PSNR gains, and average 0.57 dB PSNR gains. For normal image codec, the proposed scheme outperforms up 2.03 dB PSNR gains, and average 1.31 dB PSNR gains.