Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.39423

FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss

Hina Otake·Shinei Arakawa·Keitaro Tanaka·Yoshihiro Fukuhara·Hirokatsu Kataoka·Shigeo Morishima
View original source ↗Read source document ↗Export metadata ↓

Abstract

Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in the generative process. We propose Formula-Driven Supervised Metric Learning (FDSML), which derives relational supervision from image-generation parameters beyond discrete class labels. FDSML combines FineFractalDB, our refined fractal dataset with higher intra-class variation and more stable structures, with a Parameter-Aware Triplet (PA-Triplet) Loss that assigns positives and negatives according to distances in the underlying Iterated Function System parameter space. This objective trains embeddings to respect parameter-derived relative similarity orderings. Experiments show that FDSML improves transferability to image retrieval while remaining competitive on downstream classification and improving robustness under several corruption settings. On ResNet-50, Recall@1 improves from 65.6% to 74.0% on Stanford Online Products and from 56.2% to 78.4% on Stanford Cars over FDSL pre-training. These results suggest that parameter-derived relational supervision provides a stronger synthetic pre-training signal than label-based classification for transfer beyond recognition.

Explore related subjects

cs.CVcs.AIcs.LGcs.NE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hina Otake, Shinei Arakawa, Keitaro Tanaka, Yoshihiro Fukuhara, Hirokatsu Kataoka, Shigeo Morishima. 2026-10-29. FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss. https://arxiv.org/abs/2610.39423

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

AdalReID: Auto-selected Knowledge Adapters for Lifelong Person Re-identification

Lifelong Person Re-Identification (LReID) extends traditional ReID by requiring systems to continually learn from non-overlapping datasets across different times and locations, adapting to new identities while preserving knowledge of previous ones. Existing approaches, either rehearsal-free or rehearsal-based, still suffer from the problem of catastrophic forgetting since they try to cram diverse knowledge into one fixed model. To overcome this limitation, we introduce a novel framework AdalReID, that adopts knowledge adapters and a parameter-free auto-selection mechanism for lifelong learning. Concretely, we incrementally build distinct adapters to learn domain-specific knowledge at each step, which can effectively learn and preserve knowledge across different datasets. Meanwhile, the proposed auto-selection strategy adaptively calculates the knowledge similarity between the input set and the adapters. On the one hand, the appropriate adapters are selected for the inputs to process ReID, and on the other hand, the knowledge interaction and fusion between adapters are enhanced to improve the generalization ability of the model. Extensive experiments are conducted to demonstrate the superiority of our AdalReID, which significantly beats SOTAs by about 5\% and 3\% mAP on seen and unseen domains.

cs.CV↗
arXiv2026

Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging

Multi-graph matching is a fundamental problem in computer vision. Our work is motivated by a challenging application in bioimaging, where dozens or even hundreds of 3D microscopy images of worms must be brought into correspondence. Existing datasets do not cover this large-scale regime, and virtually all existing methods are inapplicable because they assume a complete or dense problem setting. To support further research, our first contribution is a new large-scale dataset based on problem instances from bioimaging. Our second contribution is a comprehensive analysis of the two main multi-graph matching paradigms: direct and permutation synchronization-based formulations. We argue, in part by proof, that practical large-scale methods must explicitly address problem sparsity and incompleteness. Since standard permutation synchronization approaches fail in this setting, we further introduce a sparse permutation synchronization paradigm. Our final contribution is GREEDA, a general method for sparse and incomplete problems that can be instantiated across cost orders and paradigms. While our paper focuses on objective functions up to quadratic order, GREEDA is inherently generalizable to arbitrary orders. On larger, sparse instances, GREEDA outperforms competing methods in both objective value and runtime. For example, for moderately sized problems based on 30 worm images GREEDA produces a high-quality solution within 2 minutes, whereas competitors require at least an hour and yield far worse results. On smaller dense problems, GREEDA remains on par with leading methods while being an order of magnitude faster.

cs.CV↗
arXiv2026

Transferable Multiform Attack for Cross-Modal Person Re-Identification

Cross-modal person re-identification (ReID) adversarial attacks face a transferability challenge: existing methods may exhibit source-pair overfitting, where learned perturbations become overly dependent on the feature statistics of the source modality pair, thereby limiting their transferability under heterogeneous domain shifts. We argue that a key challenge is reducing source-pair-specific bias while maintaining attack effectiveness. To address this, we propose Multiform Attack (MA). The framework first learns a universal perturbation via Mahalanobis-guided gradient optimization. Since this perturbation may still retain source-pair-specific bias, the second stage performs sparse refinement through attention-shift-filtered multi-objective evolutionary search, leveraging heterogeneous source distributions in transfer settings. By separating universal perturbation learning from sparse evolutionary refinement, MA aims to reduce reliance on source-pair-specific patterns and improve transferability to unseen domains. Extensive experiments demonstrate strong attack effectiveness on cross-modal ReID benchmarks and improved transferability in the evaluated held-out-domain settings, with additional evaluations across model architectures.

cs.CV↗