multi-prototype matching

To address three key challenges in domain-adaptive hashing retrieval—cross-domain semantic inconsistency, unreliable pseudo-labels, and low-quality hash codes—this paper proposes a two-stage framework. In the first stage, orthogonal prototype learning is introduced to achieve class-level semantic alignment, overcoming the limitations of conventional pairwise sample alignment. In the second stage, pseudo-label reliability is measured via geometric proximity, and domain-specific quantization functions are jointly optimized with mutual approximation constraints within a reconstructed feature space to enhance hash consistency. The method integrates prototype-driven alignment, reliability-weighted pseudo-label refinement, and reconstructed-feature quantization. Extensive experiments on multiple cross-domain image retrieval benchmarks demonstrate significant improvements over state-of-the-art methods, effectively mitigating domain shift and validating the efficacy of synergistic optimization between semantic alignment and reliable quantization.

This work addresses the low efficiency and poor structural preservation inherent in prototype selection for large-scale datasets. We propose TPS, a topology-aware prototype selection framework grounded in topological data analysis (TDA). TPS leverages persistent homology to characterize the intrinsic geometry and connectivity structure of data, enabling adaptive identification of topologically salient samples as prototypes; it further supports parallel implementation. Compared with conventional methods, TPS achieves substantial data compression—retaining only 5–15% of samples on multiple synthetic and real-world benchmarks—while maintaining or improving classification accuracy by 1.2–3.8 percentage points. The approach also exhibits strong interpretability and robustness. Its core innovation lies in the first systematic integration of TDA’s structural awareness into prototype selection, thereby unifying computational efficiency, structural fidelity, and interpretability.

This study addresses the challenges of cross-task cumulative interference and inference alignment deviation in parameter-efficient fine-tuning for class-incremental learning. To this end, we propose DLEPEM, a data-free replay framework that introduces a dynamic LoRA expert allocation mechanism to construct task-specific low-rank adaptation modules, thereby mitigating catastrophic forgetting. Furthermore, a prototype ensemble matching strategy is designed to integrate prototypes from the frozen pre-trained model with those from adaptive LoRA experts, enabling reliable task-level discrimination. Extensive experiments demonstrate that DLEPEM achieves superior performance on both standard and few-shot class-incremental learning benchmarks.

To mitigate catastrophic forgetting in pretrained models under class-incremental learning, this paper proposes a Dual-Prototype Adapter (DPA) framework. Methodologically: (1) it introduces task-level lightweight adapters to avoid full-parameter fine-tuning; (2) it establishes a dual-prototype mechanism—where the original prototype enables task-index inference for dynamic adapter selection, and the enhanced prototype improves discriminability among highly correlated classes; (3) it incorporates a center-adaptation loss to jointly optimize intra-class compactness and inter-class separability. The framework supports test-time adaptive adapter selection, achieving state-of-the-art performance across multiple benchmarks. It significantly alleviates forgetting while preserving strong transferability and incremental generalization capability.

Automatic schema matching (ASM) suffers from low matching quality and excessive human intervention due to inherent complexity and uncertainty. Method: This study pioneers modeling ASM as a complex adaptive system (CAS) and introduces an agent-based modeling and simulation (ABMS) approach to construct a system-level matching framework exhibiting emergence and synergy. Departing from conventional local-rule-driven paradigms, the framework employs biologically inspired design and systems thinking to enable a paradigm shift—from atomic, isolated matching to global, self-organized coordination. Contribution/Results: The prototype tool Reflex-SMAS, built upon this framework, demonstrates significant improvements in matching accuracy and robustness across diverse scenarios. Empirical evaluation shows a reduction of over 62% in manual verification effort, confirming the dual advantages of the systemic approach: enhanced performance and substantial labor-cost savings.

This work addresses the challenges of category confusion due to inter-class similarity and the lack of spatial localization in similarity matching within few-shot object detection. To this end, the authors propose Text-anchored Semantic Masks (TSMa) and a Stage-aligned Hierarchical Autoregressive Regression (SHARe) mechanism. TSMa leverages textual features as semantic anchors to suppress style-induced interference and enhance intrinsic class discriminability. SHARe formulates bounding box regression as a multi-stage progressive refinement process, aligning the abstraction capabilities of different Vision Transformer layers with corresponding regression stages to improve localization accuracy. Notably, the method generalizes to novel categories without additional training and achieves a new state-of-the-art performance on the COCO few-shot detection benchmark, surpassing the previous best approach by 10.1 nAP.

This work addresses the degradation of embedding space coherence and model performance in federated learning caused by premature alignment of immature prototypes, particularly under highly non-IID data distributions. To mitigate this issue, the authors propose FedSAP, a novel framework that integrates a delayed-alignment curriculum scheduling mechanism with a geometry-driven proxy separation loss on the unit hypersphere. This approach enhances intra-class compactness and inter-class separability without increasing communication overhead. As the first study to formalize scheduled alignment as a general design principle in federated prototype learning, FedSAP stabilizes representation learning without introducing additional parameters and naturally extends to semi-supervised settings. Extensive experiments demonstrate consistent improvements, with up to a 4-percentage-point gain over state-of-the-art methods across three benchmark datasets, especially excelling in high-heterogeneity scenarios.

Existing prototype alignment methods in heterogeneous federated learning enforce clients with diverse architectures to align within a unified feature subspace, thereby constraining model expressiveness. This work proposes FedSAF, a novel structural alignment paradigm that shifts the alignment objective from coordinate-wise matching to preserving the consistency of inter-class relational structures. By decoupling semantic structure alignment from shared feature bases, FedSAF models class relationships through prototypes and integrates them into a distributed optimization framework. Extensive experiments demonstrate that FedSAF significantly outperforms current heterogeneous federated learning approaches across multiple benchmarks, achieving accuracy improvements of up to 3.52%.

This study addresses the interpretability limitations of traditional classification prototypes by proposing an explainable document classification method. The core idea involves rendering documents as HSV images, replacing abstract vectors with visual prototypes and performing template matching via deformable row alignment. Methodologically, this work introduces image-value prototypes and named linguistic factor channels to support end-to-end color-space learning and generative decoding. Training is further optimized through a Skip-Gram objective, a four-dimensional bottleneck, and dynamic time warping-style alignment. Experimental results demonstrate that the proposed approach outperforms vector-based prototypes while effectively validating its layout-preservation capabilities. Furthermore, the findings reveal inherent limitations of distance-based matching, thereby establishing a novel paradigm for interpretable document classification.

本文探讨使用程序作为概念的通用表示方法，以解决人类如何从稀疏数据中学习和泛化新概念的问题。