Accurately identifying DNA methylation is essential for understanding complex regulatory networks and disease mechanisms. However, the dynamic nature of methylation and species differences make prediction challenging. Existing deep learning methods often overlook the potential of shared features across diverse species' methylation sequences and rely solely on token-to-token attention when modelling long-range dependencies, limiting the model's representation capabilities. To address these limitations, we propose iDNA-DAPHA, an accurate and generic two-stage deep learning framework that leverages domain-adaptive pretraining (DAP) incorporating feature alignment to learn common features across various types of methylation sequences from multiple species, followed by fine-tuning to capture task-specific features. The framework further introduces hierarchical attention (HA) to enhance its representational power. Experimental results demonstrate that iDNA-DAPHA performs better than existing state-of-the-art methods across seventeen benchmark datasets covering three representative DNA methylation types. Ablation studies validate the effectiveness and contributions of DAP and HA. Furthermore, visualization-based analyses reveal that the model can capture conserved sequence patterns and learn discriminative representations. We believe that iDNA-DAPHA will serve as a valuable framework for methylation prediction, especially in scenarios with limited training samples for specific methylation types in certain species.

Few-shot single-view 3D reconstruction learns to reconstruct the novel category objects based on a query image and a few support shapes. However, since the query image and the support shapes are of different modalities, there is an inherent feature misalignment problem damaging the reconstruction. Previous works in the literature do not consider this problem. To this end, we propose the cross-modal feature alignment network (CMFAN) with two novel techniques. One is a strategy for model pretraining, namely, cross-modal contrastive learning (CMCL), here the 2D images and 3D shapes of the same objects compose the positives, and those from different objects form the negatives. With CMCL, the model learns to embed the 2D and 3D modalities of the same object into a tight area in the feature space and push away those from different objects, thus effectively aligning the global cross-modal features. The other is cross-modal feature fusion (CMFF), which further aligns and fuses the local features. Specifically, it first re-represents the local features with the cross-attention operation, making the local features share more information. Then, CMFF generates a descriptor for the support features and attaches it to each local feature vector of the query image with dense concatenation. Moreover, CMFF can be applied to multilevel local features and brings further advantages. We conduct extensive experiments to evaluate the effectiveness of our designs, and CMFAN sets new state-of-the-art performance in all of the 1-/10-/25-shot tasks of ShapeNet and ModelNet datasets.

Few-shot point cloud classification is currently an under-explored problem which aims to learn a point cloud classifier for novel categories given a few annotated training data. Most existing methods achieve classification by matching a query point cloud to the most similar support category at the global representation level. However, due to the complicated structure of the point cloud and scarce available training data, the global representations of the point clouds and categories are of low quality, limiting the matching accuracy. Therefore, in this paper, we propose the Component-Aware Matching Network (CMNet) that matches the point clouds at the component level in addition to the global level. Specifically, we construct a component set for each query point cloud and support category and develop a metric to measure the similarity between two component sets. The final prediction is the weighted sum of the global and component matching probabilities. Besides, we carefully devise a component matching pretraining scheme for CMNet to enhance its ability to extract component features, further improving its performance. To evaluate the effectiveness of our design, we conduct comprehensive experiments on three benchmarks, namely ModelNet-FS, ShapeNet-FS and Shrec-FS. As a result, CMNet consistently outperforms the existing methods with significant margins in all the experiments of the three benchmarks and sets new state-of-the-art performance.

Single-view point cloud reconstruction aims to generate a 3D point cloud of an object given one 2D image taken from an arbitrary viewpoint. Most previous works assume that all the test categories have been present to the model during training. However, it is impossible to know all the test categories that the model will meet in advance. And we discover these methods can not deal with novel categories well. Therefore, in this article, we investigate a more realistic and challenging setting of single-view point cloud reconstruction, zero-shot, where the model's performance on novel categories is pursued. Towards this task, we propose the Cross-Category Knowledge Transferring Network (CCKTN), which maintains a knowledge bank to mine transferable knowledge from known categories to help reconstruct novel categories. Additionally, we conduct auxiliary learning for the point cloud reconstruction model with the point cloud autoencoder via sharing the same knowledge bank. This design enables the knowledge bank to collect more fruitful 3D knowledge of point clouds. Moreover, we devise a diversity loss regularization for the knowledge vectors to guarantee their diversities, further enhancing CCKTN's performance. Comprehensive experiments conducted on ShapeNet and ModelNet datasets show CCKTN's superiority towards existing methods and demonstrate CCKTN's effectiveness for reconstructing novel category objects.