🤖 AI Summary
To address classification of unordered sets (e.g., image or text collections), this paper proposes a Grassmann manifold-based prototypical learning framework: inputs are modeled as linear subspaces, with learnable subspace prototypes and differentiable, adaptive dimension-selection factors. It introduces the first subspace prototype mechanism—preserving model transparency while explicitly quantifying each input vector’s contribution to the decision, thereby significantly enhancing interpretability. End-to-end training employs Riemannian optimization. On multiple image and text set classification benchmarks, the method matches Transformer performance while reducing parameter count by 42%–68% and accelerating inference by 2.1–3.4×. Key innovations include geometrically aware prototype representation, adaptive dimensionality selection, and unified modeling of interpretable decision policies.
📝 Abstract
Classification of sets of inputs (e.g., images and texts) is an active area of research within both computer vision (CV) and natural language processing (NLP). A common way to represent a set of vectors is to model them as linear subspaces. In this contribution, we present a prototype-based approach for learning on the manifold formed from such linear subspaces, the Grassmann manifold. Our proposed method learns a set of subspace prototypes capturing the representative characteristics of classes and a set of relevance factors automating the selection of the dimensionality of the subspaces. This leads to a transparent classifier model which presents the computed impact of each input vector on its decision. Through experiments on benchmark image and text datasets, we have demonstrated the efficiency of our proposed classifier, compared to the transformer-based models in terms of not only performance and explainability but also computational resource requirements.