Liping Yu

Wei Hui, Liping Yu, Yiran Wei

Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large number of training data. The problem is that this kind of object-oriented learning lacks a priori knowledge. The amount of training data and the complexity of computations are very large, and it is hard to extract explicit knowledge after learning. This is typically called "knowing how without knowing why". We adopted a method of inductive learning, hoping to derive conceptual knowledge of the shape of an object and its formal representation based on a small number of positive examples. It is clear that implementing object recognition is not based on simple physical features such as colors, edges, textures, etc., but on their common geometry, such as topologies, which are stable, persistent, and essential to recognition. In this paper, a formal representation of topological structure based on object's skeleton (RTS) was proposed and the induction process of "seeking common ground" is realized. This research helps promote the method of object recognition from empiricism to rationalism.

Meihua Zhou, Liping Yu, Jiawei Cai et al.

Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal component analysis (PCA)-based spectral downsampling. By gradually introducing spectral complexity while preserving essential information, SpectralTrain enables efficient learning of spectral -- spatial patterns at significantly reduced computational costs. The framework is independent of specific architectures, optimizers, or loss functions and is compatible with both classical and state-of-the-art (SOTA) models. Extensive experiments on three benchmark datasets -- Indian Pines, Salinas-A, and the newly introduced CloudPatch-7 -- demonstrate strong generalization across spatial scales, spectral characteristics, and application domains. The results indicate consistent reductions in training time by 2-7x speedups with small-to-moderate accuracy deltas depending on backbone. Its application to cloud classification further reveals potential in climate-related remote sensing, emphasizing training strategy optimization as an effective complement to architectural design in HSI models. Code is available at https://github.com/mh-zhou/SpectralTrain.