As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount. Existing methods often resort to post-hoc techniques or prototypes to explain the decision-making process, which can be indirect and lack intrinsic illustration. In this research, we introduce ViTree, a novel approach for fine-grained visual categorization that combines the popular vision transformer as a feature extraction backbone with neural decision trees. By traversing the tree paths, ViTree effectively selects patches from transformer-processed features to highlight informative local regions, thereby refining representations in a step-wise manner. Unlike previous tree-based models that rely on soft distributions or ensembles of paths, ViTree selects a single tree path, offering a clearer and simpler decision-making process. This patch and path selectivity enhances model interpretability of ViTree, enabling better insights into the model's inner workings. Remarkably, extensive experimentation validates that this streamlined approach surpasses various strong competitors and achieves state-of-the-art performance while maintaining exceptional interpretability which is proved by multi-perspective methods. Code can be found at https://github.com/SJTU-DeepVisionLab/ViTree.

Graph Neural Networks (GNNs) are one of the prominent methods to solve semi-supervised learning on graphs. However, most of the existing GNN models often need sufficient observed data to allow for effective learning and generalization. In real-world scenarios where complete input graph structure and sufficient node labels might not be achieved easily, GNN models would encounter with severe performance degradation. To address this problem, we propose WSGNN, short for weakly-supervised graph neural network. WSGNN is a flexible probabilistic generative framework which harnesses variational inference approach to solve graph semi-supervised learning in a label-structure joint estimation manner. It collaboratively learns task-related new graph structure and node representations through a two-branch network, and targets a composite variational objective derived from underlying data generation distribution concerning the inter-dependence between scarce observed data and massive missing data. Especially, under weakly-supervised low-data regime where labeled nodes and observed edges are both very limited, extensive experimental results on node classification and link prediction over common benchmarks demonstrate the state-of-the-art performance of WSGNN over strong competitors. Concretely, when only 1 label per class and 1% edges are observed on Cora, WSGNN maintains a decent 52.00% classification accuracy, exceeding GCN by 75.6%.