Metric Learning

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Metric learning studies how to shape similarity so that distances in a representation reflect the relationships that matter for a task. Common approaches include contrastive, triplet, proxy, and angular-margin losses, alongside learned Mahalanobis, manifold, and optimal-transport distances. These methods support image and cross-modal retrieval, person re-identification, few-shot classification, clustering, and open-set recognition, with evaluations often measuring retrieval ranking, recognition accuracy, and robustness to domain shift. A central challenge is making learned geometries expressive enough to capture task-specific structure while remaining stable under scarce or noisy labels and generalizing to unfamiliar data.

Latest papers

80 papers8 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.

Apr 4, 2026

Task-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations

Prior multi-task triplet loss methods relied on static weights to balance supervision between various types of annotation. However, static weighting requires tuning and does not account for how tasks interact when shaping a shared representation. To address this, the proposed task-guided multi-annotation triplet loss removes this dependency by selecting triplets through a mutual-information criteria that identifies triplets most informative across tasks. This strategy modifies which samples influence the representation rather than adjusting loss magnitudes. Experiments on an aerial wildlife dataset compare the proposed task-guided selection against several triplet loss setups for shaping a representation in an effective multi-task manner. The results show improved classification and regression performance and demonstrate that task-aware triplet selection produces a more effective shared representation for downstream tasks.

Meilun Zhou, Alina Zare

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA, 32611 · Artificial Intelligence and Informatics Research Institute, University of Florida, Gainesville, FL, USA, 32611

Representation Learning Multi-Task Learning Geospatial Representation Learning Metric Learning Multi-Task Optimization

Mar 27, 2026

Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training

The Triplet Margin Ranking Loss is one of the most widely used loss functions in Siamese Networks for solving Distance Metric Learning (DML) problems. This loss function depends on a margin parameter μ, which defines the minimum distance that should separate positive and negative pairs during training. In this work, we show that, during training, the effective margin of many triplets often exceeds the predefined value of μ, provided that a sufficient number of triplets violating this margin is observed. This behavior indicates that fixing the margin throughout training may limit the learning process. Based on this observation, we propose a margin scheduler that adjusts the value of μ according to the proportion of easy triplets observed at each epoch, with the goal of maintaining training difficulty over time. We show that the proposed strategy leads to improved performance when compared to both a constant margin and a monotonically increasing margin scheme. Experimental results on four different datasets show consistent gains in verification performance.

Roberto Sprengel Minozzo Tomchak, Oge Marques, Lucas Garcia Pedroso +2

Departamento de Informática, Universidade Federal do Paraná, Curitiba, Brazil · College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, U.S.A · Departamento de Matemática, Universidade Federal do Paraná, Curitiba, Brazil

Metric Learning Margin-Based Learning Siamese Neural Networks

Mar 27, 2026

Silhouette Loss: Differentiable Global Structure Learning for Deep Representations

Learning discriminative representations is a central goal of supervised deep learning. While cross-entropy (CE) remains the dominant objective for classification, it does not explicitly enforce desirable geometric properties in the embedding space, such as intra-class compactness and inter-class separation. Existing metric learning approaches, including supervised contrastive learning (SupCon) and proxy-based methods, address this limitation by operating on pairwise or proxy-based relationships, but often increase computational cost and complexity. In this work, we introduce Soft Silhouette Loss, a novel differentiable objective inspired by the classical silhouette coefficient from clustering analysis. Unlike pairwise objectives, our formulation evaluates each sample against all classes in the batch, providing a batch-level notion of global structure. The proposed loss directly encourages samples to be closer to their own class than to competing classes, while remaining lightweight. Soft Silhouette Loss can be seamlessly combined with cross-entropy, and is also complementary to supervised contrastive learning. We propose a hybrid objective that integrates them, jointly optimizing local pairwise consistency and global cluster structure. Extensive experiments on seven diverse datasets demonstrate that: (i) augmenting CE with Soft Silhouette Loss consistently improves over CE and other metric learning baselines; (ii) the hybrid formulation outperforms SupCon alone; and (iii) the combined method achieves the best performance, improving average top-1 accuracy from 36.71% (CE) and 37.85% (SupCon2) to 39.08%, while incurring substantially lower computational overhead. These results suggest that classical clustering principles can be reinterpreted as differentiable objectives for deep learning, enabling efficient optimization of both local and global structure in representation spaces.

Matheus Vinícius Todescato, Joel Luís Carbonera

Institute of Informatics UFRGS Porto Alegre, Brazil

Representation Learning Metric Learning

Mar 27, 2026

Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs

Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generating captions to form a suboptimal feature->caption->feature pipeline and adopt metric learning solely within the visual space. In this paper, we propose FSAR-LLaVA, the first end-to-end method to leverage MLLMs (such as Video-LLaVA) as a multimodal knowledge base for directly enhancing FSAR. First, at the feature level, we leverage the MLLM's multimodal decoder to extract spatiotemporally and semantically enriched representations, which are then decoupled and enhanced by our Multimodal Feature-Enhanced Module into distinct visual and textual features that fully exploit their semantic knowledge for FSAR. Next, we leverage the versatility of MLLMs to craft input prompts that flexibly adapt to diverse scenarios, and use their aligned outputs to drive our designed Composite Task-Oriented Prototype Construction, effectively bridging the distribution gap between meta-train and meta-test sets. Finally, to enable multimodal features to guide metric learning jointly, we introduce a training-free Multimodal Prototype Matching Metric that adaptively selects the most decisive cues and efficiently leverages the decoupled feature representations produced by MLLMs. Extensive experiments demonstrate superior performance across various tasks with minimal trainable parameters.

Jiazheng Xing, Chao Xu, Hangjie Yuan +4

College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China. · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China. · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China. +1

Few-Shot Learning Multimodal Learning Metric Learning

Mar 26, 2026

Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning

Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the world's population. Timely identification of these diseases enables more effective intervention and significantly reduces the risk of large-scale crop losses. However, traditional deep learning models primarily rely on cross entropy loss, which often struggles with high intra-class variance and inter-class similarity, common challenges in plant pathology datasets. To tackle this, we propose a dual-loss framework that combines Center Loss and ArcFace Loss to enhance fine-grained classification of rice leaf diseases. The method is applied into three state-of-the-art backbone architectures: InceptionNetV3, DenseNet201, and EfficientNetB0 trained on the public Rice Leaf Dataset. Our approach achieves significant performance gains, with accuracies of 99.6%, 99.2% and 99.2% respectively. The results demonstrate that angular margin-based and center-based constraints substantially boost the discriminative strength of feature embeddings. In particular, the framework does not require major architectural modifications, making it efficient and practical for real-world deployment in farming environments.

Md. Rokon Mia, Rakib Hossain Sajib, Abdullah Al Noman +2

1,2Department of Computer Science and Engineering, Begum Rokeya University, Rangpur, Rangpur, Bangladesh · 3Wilmington University, New Castle, Delaware, United States · 4Washington University of Science and Technology, Alexandria, Virginia, United States +1

Plant Disease Classification Fine-Grained Image Classification Metric Learning

Mar 24, 2026

Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-centric variables, the method learns a latent representation in which points belonging to the same object are embedded nearby while unrelated points are separated. Clusters are then reconstructed using a density-based readout in the learned metric space, decoupling representation learning from cluster formation and enabling flexible inference. The approach is evaluated on simulated data from a highly granular calorimeter, where the task is to separate highly overlapping particle showers represented as sets of calorimeter hits. A direct comparison with object condensation (OC) is performed using identical graph neural network backbones and equal latent dimensionality, isolating the effect of the learning objective. The CML method produces a more stable and separable embedding geometry for both electromagnetic and hadronic particle showers, leading to improved local neighbourhood consistency, a more reliable separation of overlapping showers, and better generalization when extrapolating to unseen multiplicities and energies. This translates directly into higher reconstruction efficiency and purity, particularly in high-multiplicity regimes, as well as improved energy resolution. In mixed-particle environments, CML maintains strong performance, suggesting robust learning of the shower topology, while OC exhibits significant degradation. These results demonstrate that similarity-based representation learning combined with density-based aggregation is a promising alternative to object-centric approaches for point cloud segmentation in highly granular detectors.

Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer +3

Blackett Laboratory, Imperial College London, UK · Department of Physics & Astronomy, Brown University, USA · Brown Center for Theoretical Physics and Innovation (BCTPI), Brown University, USA

Density-Based Clustering 3D Point Cloud Segmentation Clustering Supervised Contrastive Learning Metric Learning

Mar 23, 2026

Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning

Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting source embeddings are independent of speaker traits. However, this assumption remains unverified. In this paper, we first investigate the impact of speaker factors on source verification. We propose a speaker-disentangled metric learning (SDML) framework incorporating two novel loss functions. The first leverages Chebyshev polynomial to mitigate gradient instability during disentanglement optimization. The second projects source and speaker embeddings into hyperbolic space, leveraging Riemannian metric distances to reduce speaker information and learn more discriminative source features. Experimental results on MLAAD benchmark, evaluated under four newly proposed protocols designed for source-speaker disentanglement scenarios, demonstrate the effectiveness of SDML framework. The code, evaluation protocols and demo website are available at https://github.com/xxuan-acoustics/RiemannSD-Net.

Xi Xuan, Wenxin Zhang, Zhiyu Li +3

University of Eastern Finland, Finland · University of Illinois Urbana-Champaign, USA · University of Chinese Academy of Sciences, China +2

Speaker Disentanglement Metric Learning Audio Deepfake Detection

Mar 16, 2026

Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition

The pooling layer plays a vital role in aggregating local descriptors into the metrizable global descriptor in the LiDAR Place Recognition (LPR). In particular, the second-order pooling is capable of capturing higher-order interactions among local descriptors. However, its existing methods in the LPR adhere to conventional implementations and post-normalization, and incur the descriptor unsuitable for Euclidean distancing. Based on the recent interpretation that associates NetVLAD with the second-order statistics, we propose to integrate second-order pooling with the inductive bias from Voronoi cells. Our novel pooling method aggregates local descriptors to form the second-order matrix and whitens the global descriptor to implicitly measure the Mahalanobis distance while conserving the cluster property from Voronoi cells, addressing its numerical instability during learning with diverse techniques. We demonstrate its performance gains through the experiments conducted on the Oxford Robotcar and Wild-Places benchmarks and analyze the numerical effect of the proposed whitening algorithm.

Jaein Kim, Hee Bin Yoo, Dong-Sig Han +1

Interdisciplinary Program in Neuroscience, Seoul National University · Département d’Informatique, École Normale Supérieure (ENS) · Department of Computing, Imperial College London +1

Feature Whitening LiDAR Place Recognition Metric Learning Point Cloud Learning

Mar 15, 2026

$K-$ means with learned metrics

We study the Fréchet

k-

means of a metric measure space when both the measure and the distance are unknown and have to be estimated. We prove a general result that states that the

k-

means are continuous with respect to the measured Gromov-Hausdorff topology. In this situation, we also prove a stability result for the Voronoi clusters they determine. We do not assume uniqueness of the set of

k-

means, but when it is unique, the results are stronger. This framework provides a unified approach to proving consistency for a wide range of metric learning procedures. As concrete applications, we obtain new consistency results for several important estimators that were previously unestablished, even when

k=1

. These include

k-

means based on: (i) Isomap and Fermat geodesic distances on manifolds, (ii) difussion distances, (iii) Wasserstein distances computed with respect to learned ground metrics. Finally, we consider applications beyond the statistical inference paradigm like (iv) first passage percolation and (v) discrete approximations of length spaces.

Pablo Groisman, Matthieu Jonckheere, Jordan Serres +1

Clustering Metric Learning

Mar 14, 2026

Scribe Verification in Chinese manuscripts using Siamese, Triplet, and Vision Transformer Neural Networks

The paper examines deep learning models for scribe verification in Chinese manuscripts. That is, to automatically determine whether two manuscript fragments were written by the same scribe using deep metric learning methods. Two datasets were used: the Tsinghua Bamboo Slips Dataset and a selected subset of the Multi-Attribute Chinese Calligraphy Dataset, focusing on the calligraphers with a large number of samples. Siamese and Triplet neural network architectures are implemented, including convolutional and Transformer-based models. The experimental results show that the MobileNetV3+ Custom Siamese model trained with contrastive loss achieves either the best or the second-best overall accuracy and area under the Receiver Operating Characteristic Curve on both datasets.

Dimitrios-Chrysovalantis Liakopoulos, Yanbo Zhang, Chongsheng Zhang +1

Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece · School of Computer and Information Engineering, Henan University, Kaifeng, China

Historical Document Analysis Siamese Neural Networks Metric Learning

Mar 13, 2026

Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval

We propose the Deep Distance Measurement Method (DDMM) to improve retrieval accuracy in unsupervised multivariate time series similarity retrieval. DDMM enables learning of minute differences within states in the entire time series and thereby recognition of minute differences between states, which are of interest to users in industrial plants. To achieve this, DDMM uses a learning algorithm that assigns a weight to each pair of an anchor and a positive sample, arbitrarily sampled from the entire time series, based on the Euclidean distance within the pair and learns the differences within the pairs weighted by the weights. This algorithm allows both learning minute differences within states and sampling pairs from the entire time series. Our empirical studies showed that DDMM significantly outperformed state-of-the-art time series representation learning methods on the Pulp-and-paper mill dataset and demonstrated the effectiveness of DDMM in industrial plants. Furthermore, we showed that accuracy can be further improved by linking DDMM with existing feature extraction methods through experiments with the combined model.

Susumu Naito, Kouta Nakata, Yasunori Taguchi

Corporate Laboratory, Toshiba Corporation, Kawasaki, Japan

Time Series Representation Learning Metric Learning

Mar 10, 2026

Geometry-Aware Metric Learning for Cross-Lingual Few-Shot Sign Language Recognition on Static Hand Keypoints

Sign language recognition (SLR) systems typically require large labeled corpora for each language, yet the majority of the world's 300+ sign languages lack sufficient annotated data. Cross-lingual few-shot transfer, pretraining on a data-rich source language and adapting with only a handful of target-language examples, offers a scalable alternative, but conventional coordinate-based keypoint representations are susceptible to domain shift arising from differences in camera viewpoint, hand scale, and recording conditions. This shift is particularly detrimental in the few-shot regime, where class prototypes estimated from only K examples are highly sensitive to extrinsic variance. We propose a geometry-aware metric-learning framework centered on a compact 20-dimensional inter-joint angle descriptor derived from MediaPipe static hand keypoints. These angles are invariant to SO(3) rotation, translation, and isotropic scaling, eliminating the dominant sources of cross-dataset shift and yielding tighter, more stable class prototypes. Evaluated on four fingerspelling alphabets spanning typologically diverse sign languages, ASL, LIBRAS, Arabic Sign Language, and Thai Sign Language, the proposed angle features improve over normalized-coordinate baselines by up to 25 percentage points within-domain and enable frozen cross-lingual transfer that frequently exceeds within-domain accuracy, using a lightweight MLP encoder with about 10^5 parameters. These findings demonstrate that invariant hand-geometry descriptors provide a portable and effective foundation for cross-lingual few-shot SLR in low-resource settings.

Chayanin Chamachot, Kanokphan Lertniponphan

Department of Computer Engineering Faculty of Engineering, Chulalongkorn University

Sign Language Recognition Cross-Lingual Transfer Metric Learning Geometric Representation Learning

Mar 3, 2026

Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering

Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their values in well-defined Euclidean distance space, categorical attribute values are different concepts (e.g., different occupations) embedded in an implicit space. Simultaneously exploiting these two very different types of information is an unavoidable but challenging problem, and most advanced attempts either encode the heterogeneous numerical and categorical attributes into one type, or define a unified metric for them for mixed data clustering, leaving their inherent connection unrevealed. This paper, therefore, studies the connection among any-type of attributes and proposes a novel Heterogeneous Attribute Reconstruction and Representation (HARR) learning paradigm accordingly for cluster analysis. The paradigm transforms heterogeneous attributes into a homogeneous status for distance metric learning, and integrates the learning with clustering to automatically adapt the metric to different clustering tasks. Differing from most existing works that directly adopt defined distance metrics or learn attribute weights to search clusters in a subspace. We propose to project the values of each attribute into unified learnable multiple spaces to more finely represent and learn the distance metric for categorical data. HARR is parameter-free, convergence-guaranteed, and can more effectively self-adapt to different sought number of clusters

k

. Extensive experiments illustrate its superiority in terms of accuracy and efficiency.

Yiqun Zhang, Mingjie Zhao, Yizhou Chen +2

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China · Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, 361102, Fujian, China · Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China

Clustering Unsupervised Clustering Metric Learning

Mar 3, 2026

Learning Order Forest for Qualitative-Attribute Data Clustering

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method.

Mingjie Zhao, Sen Feng, Yiqun Zhang +3

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China · Department of Computer Science, Hong Kong Baptist University, Hong Kong, China · Guangdong Laboratory of Artiﬁcial Intelligence and Digital Economy (SZ), Shenzhen, China +3

Clustering Metric Learning

Mar 3, 2026

Implicit Bias in Deep Linear Discriminant Analysis

While the Implicit Bias(or Implicit Regularization) of standard loss functions has been studied, the optimization geometry induced by discriminative metric-learning objectives remains largely unexplored.To the best of our knowledge, this paper presents an initial theoretical analysis of the implicit regularization induced by the Deep LDA,a scale invariant objective designed to minimize intraclass variance and maximize interclass distance. By analyzing the gradient flow of the loss on a L-layer diagonal linear network, we prove that under balanced initialization, the network architecture transforms standard additive gradient updates into multiplicative weight updates, which demonstrates an automatic conservation of the (2/L) quasi-norm.

Jiawen Li

School of Computer Science and Engineering, University of New South Wales, Kensington, Sydney 2052, New South Wales, Australia

Implicit Regularization Metric Learning

Mar 2, 2026

ROSER: Few-Shot Robotic Sequence Retrieval for Scalable Robot Learning

A critical bottleneck in robot learning is the scarcity of task-labeled, segmented training data, despite the abundance of large-scale robotic datasets recorded as long, continuous interaction logs. Existing datasets contain vast amounts of diverse behaviors, yet remain structurally incompatible with modern learning frameworks that require cleanly segmented, task-specific trajectories. We address this data utilization crisis by formalizing robotic sequence retrieval: the task of extracting reusable, task-centric segments from unlabeled logs using only a few reference examples. We introduce ROSER, a lightweight few-shot retrieval framework that learns task-agnostic metric spaces over temporal windows, enabling accurate retrieval with as few as 3-5 demonstrations, without any task-specific training required. To validate our approach, we establish comprehensive evaluation protocols and benchmark ROSER against classical alignment methods, learned embeddings, and language model baselines across three large-scale datasets (e.g., LIBERO, DROID, and nuScenes). Our experiments demonstrate that ROSER consistently outperforms all prior methods in both accuracy and efficiency, achieving sub-millisecond per-match inference while maintaining superior distributional alignment. By reframing data curation as few-shot retrieval, ROSER provides a practical pathway to unlock underutilized robotic datasets, fundamentally improving data availability for robot learning.

Zillur Rahman, Eddison Pham, Alejandro Daniel Noel +1

University of Nevada, Las Vegas · University of Toronto · LatentWorlds AI, NL

Data-Efficient Robot Learning Few-Shot Learning Metric Learning

Feb 28, 2026

Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling

Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassification. To address this issue, we propose a Label-guided Distance Scaling (LDS) strategy. The core of our method is exploiting label semantics as supervision signals in both the training and testing stages. Specifically, in the training stage, we design a label-guided loss to inject label semantic information, pulling closer the sample representations and corresponding label representations. In the testing stage, we propose a Label-guided Scaler which scales sample representations with label semantics to provide additional supervision signals. Thus, even if labeled sample representations are far from class centers, our Label-guided Scaler pulls them closer to their class centers, thereby mitigating the misclassification. We combine two common meta-learners to verify the effectiveness of the method. Extensive experimental results demonstrate that our approach significantly outperforms state-of-the-art models. All datasets and codes are available at https://anonymous.4open.science/r/Label-guided-Text-Classification.

Yunlong Gao, Xinyue Liu, Yingbo Wang +2

Dalian University of Technology, Dalian, China

Few-Shot Text Classification Text Classification Meta-Learning Metric Learning

Feb 27, 2026

Geodesic Semantic Search: Cartographic Navigation of Citation Graphs with Learned Local Riemannian Maps

We present Geodesic Semantic Search (GSS), a retrieval system that learns node-specific Riemannian metrics on citation graphs to enable geometry-aware semantic search. Unlike standard embedding-based retrieval that relies on fixed Euclidean distances, \gss{} learns a low-rank metric tensor

\mL_i \in \R^{d \times r}

at each node, inducing a local positive semi-definite metric

\mG_i = \mL_i \mL_i^\top + \eps \mI

. This parameterization guarantees valid metrics while keeping the model tractable. Retrieval proceeds via multi-source Dijkstra on the learned geodesic distances, followed by Maximal Marginal Relevance reranking and path coherence filtering. On citation prediction benchmarks with 169K arXiv papers, GSS achieves 23% relative improvement in Recall@20 over SPECTER+FAISS baselines. We provide a Bridge Recovery Guarantee characterizing when geodesic retrieval qualitatively outperforms direct similarity, a margin separation result connecting training loss to retrieval quality, and characterize the expressiveness of low-rank metric parameterization. Our hierarchical coarse-to-fine search with k-means pooling reduces computational cost by

4\times

while maintaining 97% retrieval quality.

Brandon Yee, Lucas Wang, Kundana Kommini

Management Sciences Lab, Yee Collins Research Group

Riemannian Metric Learning Graph-Based Retrieval Metric Learning Graph Representation Learning

Feb 20, 2026

CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras

City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift while complying with data protection rules that prevent sharing raw imagery. We introduce CityGuard, a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance. The framework integrates three components. A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness. Spatially conditioned attention injects coarse geometry, such as GPS or deployment floor plans, into graph-based self-attention to enable projectively consistent cross-view alignment using only coarse geometric priors without requiring survey-grade calibration. Differentially private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment. Together these designs produce descriptors robust to viewpoint variation, occlusion, and domain shifts, and they enable a tunable balance between privacy and utility under rigorous differential-privacy accounting. Experiments on Market-1501 and additional public benchmarks, complemented by database-scale retrieval studies, show consistent gains in retrieval precision and query throughput over strong baselines, confirming the practicality of the framework for privacy-critical urban identity matching.

Rong Fu, Yibo Meng, Rui Lu +2

Independent Researcher

Person Re-Identification Metric Learning

Dec 4, 2023

Metric Learning with Quantum Feature Spaces

The

2^N

dimensions of Hilbert space give quantum metric learning exponentially many mutually orthogonal states with which to separate classes, but the same exponential dimension also degrades its optimization landscape and the tractability of evaluating its metric. By letting a classical network predict all gate parameters of a fixed ansatz, we isolate the reachable state manifold and its fidelity metric. We prove that this separability is already present on the unentangled manifold

\mathbb{M}_{\text{unentangled}} = (\mathbb{CP}^1)^N

, which saturates the

2^N

maximal orthogonal capacity needed for classification, while entanglement adds states only at intermediate separations, which are costly to resolve under finite sampling. The same size carries costs. Global fidelity causes exponential gradient collapse across both unentangled (

Θ(c^{-N})

) and entangled (

Θ(4^{-N})

) manifolds; as fidelities concentrate, the fidelity signal-to-noise ratio at a fixed shot budget also decays exponentially in

N

; and because

\mathbb{M}_{\text{unentangled}}

already holds

2^N

orthogonal states, expressive, unconstrained encoders under contrastive losses reach trivial minima by geometric shattering even without entanglement. We introduce Quantum Projective Metric Learning (QPMeL), an exact

O(N)

closed-form kernel on

\mathbb{M}_{\text{unentangled}}

that circumvents circuit simulation. QPMeL resolves gradient collapse via a curriculum annealing from a

1

-local observable regime (restoring

Θ(1/N^2)

polynomial variance) to the global product kernel. Empirically, QPMeL as a complete design exceeds eight and matches two of 11 published quantum few-shot designs.

Vinayak Sharma, Lord Sen, Ashish Padhy +4

School of Computing and Augmented Intelligence Arizona State University, Tempe, AZ 85281 · National Institute of Technology, Rourkela Sector 1, Rourkela, Odisha - 769008

Quantum Machine Learning Metric Learning Quantum Kernel Methods Quantum Circuit Trainability Quantum Feature Maps