Prototype-Based Classification

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8 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.

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Latest papers 54

Oct 8, 2026cs.CV

ProtoSemImage: Image-Valued Prototypes with Deformable Row Alignment for Interpretable Document Classification

Prototypes in classification models are almost always vectors, and a vector has no readable form. This paper asks what happens when a prototype is an image. Documents give the question a natural form, because a document can be rendered as a multi-channel image in which every token becomes a pixel, so a class representative can take the same shape and the same channel semantics as the inputs it stands for. ProtoSemImage represents each class by one or more visual archetypes: prototype images in a four-channel HSV space whose channels carry named linguistic factors. A Skip-Gram objective learns that color space end to end through a four-dimensional bottleneck, discourse boundary rows become differentiable typed difference rows, and classification reduces to 2D visual template matching: a deformable row alignment between a document image and the archetype bank, in the spirit of dynamic time warping. Because the match is a spatial pattern comparison rather than a linear readout, the model reports where an input departs from its archetype and along which channel, and a generative head decodes each archetype back into text. The image representation works: it beats an otherwise identical model with vector prototypes in all three paired seeds, by between 4.3 and 11.8 points on a ten-class task. The distance-based matching does not. A diagnostic that keeps the representation fixed and swaps only the classifier recovers the sequence baselines, which locates a 20.6-point shortfall in the matching rather than in the color compression, and a benchmark built so that a pair of documents shares a bag of words and differs only in arrangement confirms the layout-preservation it was designed for. We report both directions, because for a representation whose whole purpose is inspect ability, the failure modes are as informative as the gains.

Mohammad Zare, Pirooz Shamsinejadbabaki

Artificial Intelligence Lab at AriooBarzan Engineering Team Shiraz, Iran · Department of Computer Engineering and Information Technology Shiraz University of Technology, Shiraz, Iran

Text Classification Prototype-Based Classification Document Image Understanding Explainable Image Classification

Oct 6, 2026cs.RO

CAP: Codebook-Aligned Prediction for Tokenized Robot Policies

Action tokenization converts continuous robot actions into discrete symbols that can be modeled autoregressively. However, existing tokenizer-based policies typically ignore the tokenizer's learned latent code structure: after tokenization, the policy treats tokens as unrelated class indices and learns a new classifier from scratch. We show that this discarded structure is valuable. We introduce Codebook-Aligned Prediction (CAP), a method that directly reuses the tokenizer's code vectors as policy class prototypes while leaving the tokenizer and policy backbone otherwise unchanged. Across four quantizer families, three simulation benchmarks, and two real-robot tasks, CAP consistently improves task success over standard token classification heads while holding the tokenizer (and therefore its reconstruction quality) fixed. Our analysis further shows that these gains are not explained by higher token accuracy or changes in the policy head alone. Instead, reusing the tokenizer codebook provides the policy with valuable information about the tokenizer's learned latent structure across tokens, making token prediction errors more benign in action space and improving the representations learned by the policy backbone. These results suggest that action tokenizers learn useful action-aware latent structure beyond discrete targets that should be preserved when training downstream policies.

Haoran Chen, Jingtian Ji, Samuel Wheeler +2

Toyota Technological Institute at Chicago · Argonne National Laboratory

Robot Policy Learning Robot Learning Visuomotor Policy Learning Prototype-Based Classification

Oct 5, 2026cs.LG

ReMaD: Tuning-free Domain Adaptation for Classification and Out-of-Distribution Detection

We introduce Reduced-rank Mahalanobis Distance (ReMaD), a novel prototypical distance-based refinement to classification and out-of-distribution (OOD) detection using pretrained models without finetuning. We use embeddings of the target dataset to fit closed-form distribution statistics in the model's latent space which can classify in-distribution samples and detect OOD samples, all without training or prior knowledge of the OOD data. Building on prototype classification and OOD detection, we analyze the distribution properties of large pretrained models when processing new datasets; based on this analysis, we formulate a simple modification to Mahalanobis Distance to adapt models' latent space distributions to new domains by removing unused features, without the finetuning or hyperparameter searches required by other adaptation procedures. We demonstrate the efficacy of this method to adapt existing large pretrained image embedding models to new classification domains outside their trained capabilities by testing across four target datasets, with competitive performance in both classification and OOD detection.

Elijah Bolluyt, Cristina Comaniciu

Stevens Institute of Technology, Hoboken, NJ, 07030, USA

Prototype-Based Classification Mahalanobis Distance Transfer Learning Classification OOD Detection

Oct 1, 2026cs.LG

A Safe Prototype Is Not a Safety Direction: Reference Dependence and Prompt Confounds in Response-Safety Embeddings

Can response safety be scored by cosine similarity to the mean embedding of known-safe responses? A recent sleeper-agent detector proposes exactly this score, yet the raw positive-centroid rule is not identified: positive observations locate the safe class relative to an encoder origin, but do not determine which direction separates safe from unsafe responses. We audit the rule on two prompt-controlled, human-labeled corpora and one auxiliary jury-labeled source control, using four frozen encoders and prompt-grouped splits. On the human-labeled corpora the safe prototype reaches ROC-AUC 0.457-0.545, with two cells significantly below chance and one above, while an explicit safe-minus-unsafe reference reaches 0.588-0.738 on the same embeddings; on the jury control the prototype is inverted (0.358-0.405) and the reference reaches 0.754-0.793. At validation-calibrated 5% false-safe thresholds, the reference accepts more safe responses on PKU-SafeRLHF (0.153-0.263 versus 0.039-0.061 across encoders) and Aegis (0.189-0.291 versus 0.004-0.045), but not reliably on BeaverTails. A fully unlabeled held-out reference recovers part to most of the referenced ranking, much less when only 5% of the pool is unsafe, whereas 80-634 labeled unsafe responses recover most of it. Prompt-only ablations show that prompt-label composition can inflate uncontrolled evaluations. This is a bounded result about a raw positive centroid, not all one-class methods or safety-specialized guards. A class mean is a location, not necessarily a safety direction; a declared reference with enough unsafe mass identifies orientation.

Sahil Kadadekar

New York University

Prototype-Based Classification LLM Evaluation Language Model Safety Evaluation Text Embedding Evaluation Safety Filtering

Sep 30, 2026cs.LG

Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuning with pre-trained models reduces parameter overhead but can suffer from cumulative interference and suboptimal alignment between inference samples and specialized modules. We propose Dynamic LoRA-Experts and Prototype-Ensemble Matching (DLEPEM), a two-stage rehearsal-free framework. DLEPEM allocates a task-specific LoRA-Expert for each incremental task to reduce cross-task interference, then combines frozen pre-trained-model prototypes with task-adaptive LoRA-Expert prototypes for reliable task-level discrimination. Experiments on standard CIL and Few-Shot CIL benchmarks demonstrate strong performance under the evaluated protocols.

Hongwei Zhao, Rui Liu, Yansong Liu

School of Computer Science and Engineering, Beihang University

Class-Incremental Learning Prototype-Based Classification Continual Learning Low-Rank Adaptation Parameter-Efficient Fine-Tuning

Sep 30, 2026cs.LG

Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning

Class-Incremental Learning (CIL) aims to continually learn new classes while preserving prior knowledge. Parameter-efficient fine-tuning with pre-trained models enables CIL with minimal parameter updates, but existing approaches still suffer from catastrophic forgetting caused by cumulative interference and suboptimal module-sample matching at inference. We propose Hyperbolic Prototype Routing (HyPro), a rehearsal-free framework for continual learning. HyPro allocates a dedicated LoRA-Expert module to each incremental task for isolated representation learning, then projects routing features onto a Poincare ball and performs geodesic nearest-prototype matching for reliable task-level discrimination. Extensive experiments on standard CIL and Few-Shot CIL benchmarks show that HyPro consistently improves average and final-stage accuracy over strong baselines.

HongWei Zhao, Rui Liu, Yong Chen

Beihang University · Beijing University of Posts and Telecommunications

Class-Incremental Learning Prototype-Based Classification Continual Learning

Sep 28, 2026cs.LG

ProtoSeam: Lifting Classifier Training with Latent Gaussian Mixture Models

We propose a lifted reformulation of supervised classification that improves the final accuracy of standard classifiers without changing the architecture at inference time. A network

N=N_2\circ N_1

is split at a single semantic interface and one learnable prototype per class is inserted there. Training combines a quadratic consensus penalty that pulls

N_1(x)

toward the prototype of its class with a classification loss of

N_2

evaluated on samples drawn around the prototypes, whereat no gradient crosses the interface. At inference the prototypes are discarded and the unmodified network

N_2\circ N_1

is used. Across CIFAR-10, CIFAR-100, and TinyImageNet with ResNet and vision transformer backbones, lifted training improves test accuracy by up to five percentage points over variants without lifting under a shared tuning protocol. Moreover, we provide theoretical justification of those results.

Robert Lampel, Timon Klein, Sebastian Sager

Department of Mathematics, Otto von Guericke University (Magdeburg, Germany) · Max Planck Institute for Dynamics of Complex Technical Systems (Magdeburg, Germany)

Prototype-Based Classification Classification Image Classification

Sep 23, 2026cs.CV

Diverse by Design: Architectural Constraints for Prototype-Based Interpretability

Prototype-based neural networks provide inherent interpretability through case-based reasoning, yet suffer from critical limitations: prototypes converge to redundant features, fail to capture diverse semantic parts, and lack quantitative interpretability assessment. We propose Diversity-Aware Prototype Learning (DAPL), which enforces prototype diversity through architectural constraints rather than explicit regularization. Our approach leverages multi-head self-attention with strict one-to-one attention-to-prototype mapping, ensuring each prototype specializes in distinct visual features. We further introduce foreground-aware training to focus prototypes on semantically meaningful regions and develop comprehensive evaluation metrics (Coverage and Diversity) for quantitative interpretability assessment. Experiments on CUB-200-2011 demonstrate substantial improvements: DAPL with foreground-aware training achieves 81.69% accuracy with 0.596 Coverage and 0.427 Diversity, providing the best overall balance across all evaluated prototype-based methods. Code is available at https://github.com/xinmiaolin/DAPL.

Xinmiao Lin, Matthew Wright

Rochester Institute of Technology

Prototype-Based Classification Prototype Learning Explainable Image Classification Explainability Evaluation

Sep 14, 2026cs.LG

Skeletal Prototypes on Iterative Nerve Expansions

Prototype reduction replaces a training set with a smaller representation, and the established methods return a finite set of points. We propose Skeletal Prototypes on Iterative Nerve Expansions (SPINE). The model for each class is an embedded 1-complex rather than a point set. Its initial edge set is a class-conditional Mapper graph, so the data decide which localized clusters are joined. Later phases fit the vertices under a classification objective, and an observation is assigned to the class whose complex is nearest. The segments therefore enter the decision rule and not only the fitting. We evaluate SPINE on seventeen benchmark datasets under stratified 10-fold cross validation, against seven other prototype reduction methods at a matched budget. SPINE attains the highest mean accuracy and the best average rank. It is significantly better than five of the seven competitors under Wilcoxon signed-rank tests with Holm correction. A budget sweep shows that the decision rule using the entire graph segments contribute most when prototypes are scarce, while the method as a whole competes best at moderate budgets. Construction cost places SPINE with the discriminative methods, and it is faster than generalized learning vector quantization on fourteen of the seventeen datasets.

Jordan Eckert, Henry Schenck

Department of Mathematics and Statistics Auburn University Alabama, USA

Prototype-Based Classification Prototype Learning

Sep 14, 2026cs.AI

Soft Symbol Grounding for Prototypical Concepts

Neuro-symbolic models are usually trained with supervision only on final labels, leaving the intermediate concepts unobserved. Since many concept assignments are consistent with a given label, training can predict labels correctly while recovering the wrong concepts, a failure known as a reasoning shortcut. Prototypical networks reduce shortcuts by anchoring each concept to a few labeled examples, but existing methods still couple perception and reasoning through a hand-crafted, task-specific differentiable loss that must be redesigned for every task. We introduce \textbf{Soft-PNet}, which removes this loss: it reframes concept grounding as a Metropolis walk over a precomputed cache of feasible symbolic solutions, guided by a prototype distribution built from a single labeled anchor per concept, and trains against one KL objective between the prototype-weighted cache and the network's concept predictions. The objective is identical across tasks and remains applicable when the solution space cannot be enumerated. On \texttt{MNIST-EvenOdd}, Visual Sudoku, and \texttt{Kand-Logic} under scarce supervision, Soft-PNet matches loss-engineered prototypical networks at the concept and label levels and recovers concepts that soft-grounding baselines miss, with no loss engineering and lower training time.

Marcos Galván-López, Nijesh Upreti, Hiram Calvo +2

Centro de Investigación en Computación, Instituto Politécnico Nacional. · School of Informatics, University of Edinburgh.

Prototype-Based Classification Weakly Supervised Learning Neuro-Symbolic Reasoning

Sep 14, 2026cs.AI

Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

Industrial fault diagnosis often operates with only a handful of labeled fault examples, making few-shot learning attractive for sensor monitoring. Standard prototypical networks are simple and effective; however, their class prototypes may become unstable in the very-low-shot regime because each decision relies on a small support set. We propose \emph{Multi-Episode Prototypical Networks} (MEPN), which aggregate prototypes from multiple disjoint support episodes and use their mean as the final class representative, reducing prototype variance without changing the encoder architecture. We evaluate MEPN on the DeFACTO sensor dataset using five-way fault classification with synthetic bias, drift, spike, and noise faults injected into real industrial measurements. Over 100 independent runs, MEPN reaches \textbf{\SensorOneShotGcpn%} in the per-episode one-shot setting (

K\!=\!1

shot, aggregated over

N_{\text{agg}}\!=\!10

support episodes), substantially above single-episode baselines. Under an equal 10-sample support budget, MEPN and ProtoNet at

K\!=\!10

are statistically indistinguishable, confirming prototype accumulation as the mechanism rather than superior fixed-budget learning.

Mohammed Ayalew Belay, Amirshayan Haghipour, Pierluigi Salvo Rossi

Simula UiB, Bergen, Norway · Dept. of Electronic Systems, NTNU, Trondheim, Norway · Dept. of Gas Technology, SINTEF Energy Research, Trondheim, Norway

Prototype-Based Classification Fault Detection Few-Shot Learning Fault Diagnosis

Sep 3, 2026cs.CV

What Do Scan-Derived Class Prototypes Add? Disentangling Supervision, Prototype Content and Query Protocol in Recognition over Frozen Foundation Features

A scan supplies labeled images and a geometric reference. We separate their contributions in a recognizer whose scan-derived prototype matrix acts as a supervised head's fixed output layer. On T-LESS, HOPE and 18 self-collected industrial parts, we test real, random and exactly permuted prototypes, matched geometry-free classifiers, stronger appearance rules and paired background protocols. Across DINOv2-giant and MetaCLIP-H with real-background queries, the largest fused-accuracy advantage of the real prototypes over either control is one percentage point; larger differences favor controls, by up to 2.8 points in arm means. On HOPE with DINOv2-giant the head alone is 2.8 points above exact permutations (95% interval: 0.8-4.7); this advantage does not reach fusion and is not observed on MetaCLIP-H. On DINOv2-giant, matched logistic regression comes within 0.5 points of fusion on T-LESS and exceeds it on HOPE and the self-collected parts. Against white cutouts, real HOPE query backgrounds lower image-prototype accuracy by 43 points on DINOv2-giant and 13 on MetaCLIP-H. The audit separates prototype content, label supervision and query protocol.

Chenxi Tao, Hong-In Won, Seung-Kyum Choi

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA

Prototype-Based Classification Vision Foundation Models

Aug 30, 2026cs.CV

OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification

Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burdened by complex, multi-stage training pipelines and heavily rely on auxiliary regularization to prevent prototype collapse. To overcome these limitations, we introduce Orthonormal Prototype Alignment Learning (OPAL), a single-stage, end-to-end framework that simplifies interpretable classification. Our approach anchors the latent space using predefined orthonormal bases, embedding each class within a dedicated subspace spanned by fixed part-prototypes. To achieve precise part localization, OPAL enforces spatial competition across feature maps. This mechanism isolates sparse, discriminative regions, directing each prototype to consistently attend to the same semantic concept across different images. By framing classification as a direct representation alignment task, our method eliminates the need for auxiliary losses. Extensive experiments on fine-grained benchmarks demonstrate that OPAL outperforms both its non-interpretable counterparts and state-of-the-art part-prototype methods, delivering granular visual explanations by explicitly revealing the specific image regions driving every prediction. Code is available at https://github.com/ilancarretero/OPAL.

Ilán Carretero, Gustavo Jesús Angulo, Rocío del Amor +1

CVBLab, HumanTech, Universitat Politècnica de València, Valencia, Spain · CMA, Mines Paris, PSL University, Sophia Antipolis, France · Artikode Intelligence S.L., Valencia, Spain

Prototype-Based Classification Fine-Grained Image Classification Explainable Image Classification Image Classification

Aug 14, 2026eess.AS

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

We present a parameter-free episodic evaluation of nearest-centroid classification of elephant vocalisations on fixed pretrained embeddings, for the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets. We ask not which embedding yields the best classifier trained on all labelled data, but how the simplest classifier performs as the number of exemplars per class varies. There are no learnable parameters, because each class is modelled as the mean of its support embeddings and each query is assigned to the nearest centroid under squared Euclidean distance. Evaluation covers the fixed Perch (ver. 1), Perch (ver. 2) and HuBERT (base, layer 2) embeddings, alongside mel frequency cepstral coefficient (MFCC) features,

N

-way

k

-shot, under the same stratified

K

-fold cross-validation protocol as the trained classifiers. None of these embedding models was trained to distinguish elephant call types. On the smaller EV dataset the centroid classifier is markedly data-efficient. Using Perch (ver. 1) or Perch (ver. 2) embeddings it overtakes in mean average precision (mAP) the fully-trained logistic regression (LR) baseline from one or two exemplars and the recurrent baseline from two. Over the reduced set of call types on which the strongly-supervised end-to-end baseline was trained, the centroid classifier using Perch (ver. 2) embeddings overtakes that baseline in mAP as well, from two exemplars. On the larger LDC dataset the recurrent baselines retain their advantage for all considered values of

k

. Only LR is overtaken, and only in mAP. Nearest-centroid classification is therefore preferable precisely when exemplars are few and the fixed embedding already separates the call types.

Christiaan M. Geldenhuys, Thomas R. Niesler

Department of Electrical and Electronic Engineering University of Stellenbosch

Few-Shot Audio Classification Prototype-Based Classification Classification Audio Classification

Aug 13, 2026cs.CV

Class Geometry as Supervision for Sample-Efficient Open-World Detection

Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging in scarce-data settings such as biomedical and scientific imaging, where rare categories may have only a few annotated examples and fine-grained classes differ by subtle morphology. Prototype-based detectors are natural for this regime, but they typically learn class prototypes as independent anchors, ignoring relational structure among classes. We propose class-geometry supervision (CGS), a general framework that constrains learned prototype or class-representation spaces to preserve visual or semantic class dissimilarities estimated from training data. CGS introduces a dissimilarity-preserving objective that aligns pairwise distances among learned class representations with a target class-geometry matrix while retaining the standard task loss. We instantiate the same objective across prototype recognition, few-shot biomedical object detection, open-set detection, novel-class insertion, and OWOD adaptation on COCO. Experiments show that CGS improves sample efficiency in recognition and ova detection, substantially strengthens novel-class insertion, and improves unknown recall on COCO while retaining much of the known-class detection performance. Ablations show that meaningful visual geometry provides the most reliable gains, while random geometry can help novel separation but is less consistent for few-shot detection. These results suggest that relational class geometry is an effective supervisory signal for building calibrated and extensible open-world detectors under limited supervision.

Akash Rao, Zhou Chen, Revanth Reddy Palem +3

CSSE Department, Auburn University, Auburn, AL, 36849 · Department of Veterinary Pathobiology, Oklahoma State University, Stillwater, OK, 74078

Open-Vocabulary Object Detection Prototype-Based Classification Few-Shot Object Detection Open-Set Recognition Geometric Representation Learning

Aug 12, 2026cs.LG

Exemplar-based objective classification of gust-induced loads across multiple flight conditions

Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type's transient response enables physical intuition into the underlying fluid mechanics.

Paolo Olivucci, Kowshik Srivatsan, David E. Rival

Institute of Fluid Mechanics, TU Braunschweig, Hermann-Blenk Str. 37, Braunschweig, 38108, Germany.

Prototype-Based Classification

Aug 11, 2026cs.AI

Quantum Incremental Learning with Mixed State Prototypes

Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints. In the Noisy Intermediate-Scale Quantum (NISQ) era, although quantum neural networks offer advantages in feature mapping, hardware limitations restrict circuit width. Furthermore, traditional quantum classifiers are constrained by the number of orthogonal basis states, limiting their capacity to accommodate a continually growing number of categories. Thus, we introduce a novel quantum incremental learning framework based on trainable mixed-state prototypes. Its original design incorporates new classes by adding class prototypes rather than increasing the circuit width of the shared quantum backbone. The use of mixed-state prototypes is another key contribution, since they have representation capabilities to represent information than a single pure-state prototype. And the decomposable mixed-state calculation provides lower production costs and a convenient Hilbert-Schmidt (HS) distance metric for classification. Simulation results show that our model achieves high-dimensional feature concentration using a minimal number of qubits, while demonstrating lower computational complexity and robust representation in incremental learning tasks compared with classical baselines.

Yu Wu, Qianli Zhou, Xinyang Deng +3

School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China · School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China · School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371 +5

Class-Incremental Learning Prototype-Based Classification Quantum Machine Learning Quantum Neural Networks

Aug 7, 2026cs.LG

MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor

Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pixel IR sensor integrating a 16

\times

16 thermal MOSFET (TMOS) array and a RISC-V microcontroller extended with low-precision SIMD instructions, capable of on-device learning and continual adaptation for pose and gesture recognition tasks under tight memory and power constraints (

<

32kB on-chip memory,

\approx

1.5mW). To avoid the memory overheads of backpropagation and replay buffers, we adopt a prototype-based Nearest Class Mean (NCM) classifier in which a simple Convolutional Neural Network (CNN) encoder is trained and quantized offline, while class prototypes are stored and updated on the device in streaming mode. With experiments on two datasets, we show that this approach yields accuracy on par with a conventional classifier, with negligible latency overheads in both the classification and the prototype update (

<

0.29% considering both phases), effectively enabling online adaptation of the perception framework.

Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso +7

Politecnico di Torino, Turin, Italy · STMicroelectronics, Cornaredo, Italy

Prototype-Based Classification On-Device Training Gesture Recognition Online Continual Learning Edge ML

Aug 5, 2026cs.CV

Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection

Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, substantially outperforming specialized detectors in challenging in-the-wild scenarios. This finding has established DINOv3 as the dominant foundation-model baseline for subsequent improvements. However, we find that the vision-language model Perception Encoder (PE) holds greater potential for AIGI detection, because its language-aligned representation preserves high-level provenance semantics. Specifically, PE exhibits stronger local provenance organization than DINOv3 in its frozen feature space. However, semantic-agnostic linear probing fails to exploit this structure, as PE-Linear still underperforms DINOv3-Linear by 4.1% on In-the-Wild. Based on this observation, we propose Semantic Prototype Calibration (SPC), which constructs category prototypes from forensic semantic information and calibrates them with supervised data. We apply SPC to PE and refer to the resulting detector as PE-SPC. Our analysis shows that this simple design achieves stronger generalization. Across cross-generator, post-processing, and in-the-wild benchmarks, PE-SPC surpasses the previous DINOv3 baseline and achieves new state-of-the-art results.

Weihan Cai, Hao Tan, Zichang Tan +2

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · Sangfor Technologies Inc., Shenzhen 518055, China +1

Prototype-Based Classification Vision-Language Models AI-Generated Image Detection

Aug 3, 2026cs.CV

PNEC-Mamba: Prototype-Guided Positive-Negative Evidence Calibration for Hyperspectral Image Classification

In real-world hyperspectral scenes, pixel representations are often ambiguous due to factors such as spectral similarity, mixed pixels, and local context interference, which may simultaneously encode discriminative evidence and interfering information. Existing methods mainly focus on learning more powerful representations or modeling broader contexts, but rarely investigate whether the learned representations provide reliable evidence or introduce interference into classification decisions. To address this issue, we view hyperspectral image classification from the perspective of pixel-level evidence reliability modeling and propose PNEC-Mamba, a prototype-guided positive-negative evidence calibration framework. The framework progressively establishes semantic references, separates class-related evidence from interference, estimates pixel-level reliability, and performs selective calibration. First, a full-image state-space encoder extracts pixel representations, while dynamic class prototypes provide semantic references that evolve jointly with the feature space. Subsequently, positive and negative evidence is derived from pixel-prototype competition, explicitly separating discriminative cues that support classification from confusing signals associated with competing classes. Based on these evidence relationships, a multi-source uncertainty estimation strategy is introduced to assess pixel-level reliability, enabling stronger evidence calibration for uncertain regions. Finally, a full-resolution consistency refinement step is applied to recover local spatial details and improve boundary coherence in the final predictions. Extensive experiments on three benchmark datasets demonstrate that PNEC-Mamba achieves superior classification performance compared with state-of-the-art methods.

Mingzhen Xu, Can Xu, Di Wang +2

Prototype-Based Classification Hyperspectral Image Classification Uncertainty Calibration

Jul 29, 2026cs.SD

Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement

Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples, so that prototype refinement is driven primarily by known-class evidence. The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and inlierness-weighted marginal entropy maximization, while open-set rejection uses a prior-adaptive free-energy score that adjusts its threshold with the prior proportion of unknown-class samples, decoupling detection from classification. Experiments on three audio datasets show our method achieves state-of-the-art results for few-shot open-set audio classification under multiple experimental conditions.

Tianyan Deng, Yanxiong Li, Rui Gao +1

School of Mathematics South China University of Technology Guangzhou, China · School of Electronic and Information Engineering South China University of Technology Guangzhou, China

Few-Shot Audio Classification Prototype-Based Classification Open-Set Recognition Few-Shot Learning Transductive Learning Audio Classification

Jul 14, 2026cs.CV

ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations

Prototype-based networks provide inherently interpretable classification by linking predictions to learned exemplars, but their use in 3D point clouds and clinical surface-pair reasoning remains limited. We introduce ProtoPointNet, a prototype-based model for dental occlusion classification from registered upper--lower intraoral arch pairs. Each point is encoded by a 14-dimensional descriptor combining local surface geometry, curvature, and explicit inter-arch displacement and clearance, exposing occlusal relationships to prototype matching. A shared multi-task point-cloud backbone learns axis-specific prototype heads for sagittal-left, sagittal-right, vertical, transverse, and midline classification. To support limited clinical data, we train prototypes from scratch using auxiliary supervision and encoder-freeze hand-off. On Bits2Bites, ProtoPointNet achieves mean test macro-F1 of 0.724 and AUROC of 0.825, with strongest performance on vertical (F1 0.828) and sagittal-left classification (F1 0.807). Projected prototype activations localise to anatomically plausible regions, including posterior molars and premolars for cross-bite evidence and anterior incisors for bite-depth evidence. These results support prototype-based reasoning as a transparent, spatially grounded alternative to black-box 3D classifiers for dental surface-pair analysis.

George V. Jose, Thao Liang Chiam, Toby Hughes +4

Adelaide Dental School, Adelaide University, Adelaide, South Australia, Australia · Australian Institute for Machine Learning, Adelaide University, Adelaide, South Australia, Australia · School of Public Health, Adelaide University, Adelaide, South Australia, Australia

Prototype-Based Classification Point Cloud Classification Interpretable ML

Jul 1, 2026eess.AS

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes). In this study, we propose a method for Few-shot Open-set Audio Classification (FOAC), which can recognize query samples of seen classes after updating the model using a few support samples, and meanwhile reject query samples from unseen classes. We design a model consisting of an encoder and a classifier. The encoder is the backbone of a ResNet used for extracting embeddings. The classifier consists of prototype generators of few-shot classes and open-set classes. Prototypes of few-shot classes are obtained by fusing the class-discriminative information of support and query embeddings and by assigning larger weighting coefficient to representative part of the support embeddings. One prototype is generated for open-set classes using the proposed prototype generator. The encoder is trained with abundant samples of base classes in supervised manner, and then the prototypes of base classes are generated under the supervision of a joint loss. The classifier is trained using a few samples of few-shot classes in a meta-training way. Three public datasets (LS-100, NSynth-100, and FSC-89) are used to assess the performance of our method. Experiments show that our method has advantage over prior methods in AUROC and accuracy. This advantage has statistical significance for most prior methods. Our method has lower computational complexity than most prior methods. The code is at https://github.com/Jessytan/FOAC-AIFP.

Yanxiong Li, Jiaxin Tan, Qianqian Li +3

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China · Faculty of Information Technology and Communication Sciences, Tampere University, FI-33720 Tampere, Finland

Few-Shot Audio Classification Prototype-Based Classification Open-Set Recognition Few-Shot Learning Audio Classification

Jul 1, 2026cs.CV

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal screening, accurate diagnosis remains challenging due to the low prevalence and high heterogeneity of anomalies. Existing deep learning methods for prenatal tasks rely on large-scale annotated datasets, which are difficult to obtain in practice. Although few-shot learning alleviates data scarcity, it typically requires fine-tuning for new categories, limiting its practicality in resource-limited clinical settings. To address these challenges, we propose a training-free framework for multi-class prenatal US anomaly classification and localization that operates with only a few reference images per class, representing the first exploration of this setting. Our framework comprises three key components: (1) a memory bank with multi-granular prototypes that explicitly models both class-level semantics and anomaly characteristics; (2) a prototype-driven soft merging mechanism that aggregates discriminative features to detect the anomaly region; and (3) a class-aware refinement strategy that leverages prototype consistency to improve category prediction. Extensively validated on a multi-center prenatal US dataset containing 1,149 cases, with a total of 2,357 images and 9 categories, our proposed method outperforms the competitors.

Huanwen Liang, Yuhao Huang, Xiliang Zhu +6

Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China

Few-Shot Anomaly Detection Prototype-Based Classification Medical Image Anomaly Detection Anomaly Localization Anomaly Detection

Jun 29, 2026cs.CV

GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis

Prototype-based medical image classifiers present three clinical limitations: they treat findings as independent, silently amplify unsafe physician feedback, and require full retraining whenever a new finding is needed. We present GRAPE (Graph-Augmented Prototype Explanations), a unified architecture that addresses all three challenges. First, a Graph Attention Task Head models anatomical concept co-occurrence, boosting macro-F1 by +13.8,pp over the prototype baseline on TBX11K. Second, a Concept-Mismatch Safety Check - the first such mechanism in prototype-based medical classifiers - warns when the model's dominant finding inside a doctor-drawn region conflicts with the claimed label, catching 85% of erroneous annotations versus 51% for MC-Dropout with no extra inference cost. Third, Open-Vocabulary Prototype Anchoring aligns visual prototypes to clinical text, allowing a new finding to be added from a single labeled image without modifying any other component. On NIH ChestX-ray14, one Effusion example recovers full-supervision localization accuracy; on TBX11K, prototype maps achieve 2.6x better lesion localization than end-to-end baselines. All three capabilities add only +1~ms latency at interactive batch size. The project page is https://github.com/KurbanIntelligenceLab/GRAPE.

Rasul Khanbayov, Erchin Serpedin, Hasan Kurban

1Hamad Bin Khalifa University · 2Texas A&M University

Prototype-Based Classification Medical Image Classification Explainable Medical Image Analysis Medical Image Localization

Jun 25, 2026cs.CV

Beyond Points: Spherical Distributional Part Prototypes for Interpretable Classification

Prototype-based neural networks aim to provide intrinsic interpretability by grounding predictions in a small set of part prototypes. However, modern vision backbones typically operate in normalized, directional embedding spaces where each semantic part exhibits substantial intra-class variability. As a result, point prototypes often become redundant or unstable, hurting both explanation quality and robustness. We propose vMFProto, a distributional part-prototype framework that models each class as a mixture of von Mises-Fisher components on the hypersphere. Each prototype learns its own concentration, capturing part-specific variability, and we use entropic optimal transport (OT) to obtain structured patch-to-prototype assignments. A two-stage training schedule performs OT-driven prototype discovery followed by end-to-end refinement with patch-level distillation and distribution-aware diversity regularization. Experiments with frozen DINO backbones show that vMFProto achieves leading consistency and distinctiveness on CUB-200-2011 and competitive classification accuracy across CUB, Stanford Dogs, and Stanford Cars. Qualitative results confirm that vMFProto yields localized, non-redundant part evidence.

Duarte Leão, Diogo Pereira Araújo, Catarina Barata +1

Instituto Superior Técnico, Universidade de Lisboa · Carnegie Mellon University

Prototype-Based Classification Image Classification

Jun 25, 2026cs.AI

Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact. However, their complex thermo-fluid and mechanical interactions complicate fault detection, particularly when labeled fault data are scarce. In this paper, we introduce the Kalman Prototypical Network (KPN), a metric-based few-shot learning (FSL) framework specifically tailored for CCGT fault diagnosis. We model the evolution of class prototypes as latent stochastic states in a dynamic system to reduce episodic variance and improve robustness in embedding representation. Synthetic data sets generated with a high-fidelity Modelica-based dynamic simulation of an offshore CCGT system were used, simulating both normal operation and progressive leak faults under transient conditions. Application of the proposed framework on simulated leak fault detection tasks demonstrate that KPN outperforms conventional FSL methods such as Matching Networks, Relation Networks, and MAML in both accuracy and stability under varying support and query configurations. The proposed framework significantly improves training convergence and generalization by stabilizing class representations, making it well-suited for real-world CCGT fault detection where labeled data is limited.

Mohammed Ayalew Belay, Lucas Ferreira Bernardino, Adil Rasheed +2

Dept. Electronic Systems, Norwegian University of Science and Technology, 7034 Trondheim, Norway · Dept. Gas Technology, SINTEF Energy Research, 7491 Trondheim, Norway · Dept. Engineering Cybernetics, Norwegian University of Science and Technology, 7034 Trondheim, Norway

Prototype-Based Classification Few-Shot Learning Fault Diagnosis

Jun 22, 2026cs.CV

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection

Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent prototype-based similarity learning approaches enable training-free adaptation by matching query features with class prototypes. However, they suffer from two fundamental limitations: (i) class confusion arising from inter-class similarity margin collapse, and (ii) insufficient visual cues for precise localization, as similarity scores capture only class-level semantic affinity while providing limited spatial information. To address these issues, we introduce two complementary components. Text-Anchored Semantic Mask (TSMa) leverages class-level text features as semantic anchors to identify semantically aligned channels through channel-wise interaction between visual and text features. By suppressing style-induced spurious responses and emphasizing class-intrinsic signals, TSMa enlarges inter-class similarity margins and mitigates class confusion. We further propose Stage-Aligned Hierarchical Autoregressive Regression (SHARe), which reformulates localization as a hierarchical autoregressive process that progressively refines bounding boxes across multiple stages. SHARe leverages the layer-wise characteristics of ViT representations by aligning feature abstraction levels with regression stages: deeper layers guide early coarse localization, while shallower layers rich in edge and texture cues refine spatial details in later stages. Experiments on COCO demonstrate a new state of the art, outperforming the previous best by +10.1 nAP, with extensive analysis validating each component. The code is available at https://github.com/VisualScienceLab-KHU/ReSet.

KunHo Heo, Seungjae Kim, Wongyu Lee +2

Kyung Hee University, Republic of Korea

Prototype-Based Classification Few-Shot Object Detection Few-Shot Learning Object Detection

Jun 22, 2026cs.CV

PHOEBI: An Open-World Benchmark for Bacterial Identification in Phase-Contrast Microscopy

Optical microscopy (OM) enables rapid, label-free imaging of live bacteria and is the standard instrument for species identification across clinical, environmental, and industrial microbiology. Real samples, however, are routinely polymicrobial and may contain organisms never seen during training, and no computer-vision benchmark evaluates multi-label species identification from phase-contrast microscopy (PCM) of such mixtures. We introduce Phase-contrast Optical bEnchmark for Bacterial Identification (

\textbf{PHOEBI}

), a wet-lab-prepared dataset of

120{,}000

PCM images covering

40

combinations of six rod-shaped species, together with a leave-combinations-out (LCO) protocol that holds out entire species combinations, mirroring a model trained on catalogued mixtures that must recognise new ones. Under LCO, gradient-trained per-image classifiers, from fine-tuned backbones to attention-based multiple-instance learning, collapse on unseen combinations despite high in-distribution accuracy, and the failure lies in how per-image predictions are aggregated rather than in the visual representation. We propose three lightweight

\textbf{anchor-based}

decoders that read each species' presence against fixed geometric prototypes over a shared frozen tile-feature pool, and they remain stable under the same shift. Without additional training, the same features also support open-set rejection of unseen species and the discovery of a new class from unlabeled test images, with negligible disruption to the known classes.

Aaditya Baranwal, Md Jahid Hasan, Shruti Vyas

University of Central Florida

Prototype-Based Classification Multi-Label Classification Open-Set Recognition Microscopy Image Analysis

Jun 18, 2026cs.CV

HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification

Echocardiography (echo) is a widely used imaging modality for assessing cardiac function, with Left Ventricular Filling Pressure (LVFP) serving as a critical physiological marker for conditions such as heart failure. Standard LVFP classification into normal \emph{vs} elevated categories relies on the Doppler-derived

E/e'

ratio, which is operator-dependent and often unavailable in resource-limited settings, motivating methods that infer LVFP directly from B-mode echo. Existing deep learning approaches achieve high performance but remain largely black-box, limiting clinical interpretability. We propose HypOProto, a hyperbolic, ordinal prototype-based framework for interpretable LVFP classification using a frozen, explainable foundation model backbone. HypOProto arranges prototypes along the physiological

E/e'

scale, placing borderline cases near the hyperboloid root where small angular differences separate similar cases, while normal and elevated cases occupy outward positions reflecting increasing diagnostic certainty. This hyperbolic geometry encodes clinically meaningful ordinal relationships and improves interpretability. We also introduce a novel Hyperbolic Prototype Angular Separation (HyperPAS) loss, enforcing inter-class prototype separation in hyperbolic space. HypOProto achieves SOTA performance while maintaining transparency, and highlights clinically relevant regions in visualizations. This work represents the first prototype-based framework for LVFP classification in echo. Our code can be found at https://github.com/DeepRCL/HypOProto.

Victoria Wu, Nima Hashemi, Hooman Vaseli +3

Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada · Vancouver General Hospital, Vancouver, BC, Canada

Prototype-Based Classification Echocardiography Ordinal Regression Explainable Medical Image Analysis Hyperbolic Representation Learning