The paper evaluates three compact computer‑vision models—each with about 0.26 million trainable parameters—for iris presentation attack detection (PAD) under conditions not seen during training, such as new presentation attack instruments (PAIs) and environmental degradation. The models are trained from scratch without external pretraining or data augmentation and tested on the Notre Dame subset of LivDet‑Iris 2017 across known‑attack, unknown‑attack, corrupted, and pooled partitions. Results show significant performance drops when moving from known to unknown attacks, with the best model, ZACH‑ViT, achieving the lowest unknown‑attack APCER (≈47.7 %) and D‑EER (≈38.9 %), yet overall errors remain high enough to preclude deployment readiness.
By Athanasios Angelakis, Marta Gomez-Barrero
arXiv:2608.28341v1 Announce Type: new
Abstract: Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent...
By Eric L. Wisotzky, Jost Triller, Simon W. H\"artl, Oliver T. Bruns, Peter Eisert, Anna Hilsmann
OCT-FedSIR is a reliability‑aware spectral framework designed for federated learning of OCT image classification in the presence of client‑dependent annotation noise and heterogeneous data distributions. It integrates class‑balanced spectral estimation, logit adjustment, complementary spectral descriptors, selective spectral relabeling, and noise‑aware federated optimization. Across 117 experimental conditions on three datasets, OCT‑FedSIR achieved a mean accuracy of 86.73%, outperforming RoFL (79.94%) and FedCorr (78.75%) and successfully identifying and correcting corrupted annotations with high precision.
By Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Rashadul H. Badhon, Behafarin Emam, Sally S. Y. Ong, Atalie C. Thompson, Theodore Leng, Ahmed Arafa, Jennifer I. Lim, Minhaj Nur Alam
FoCal is a new frequency‑oriented framework for aerial RGB–IR object detection that explicitly models cross‑modal interaction across different frequency components. It introduces a Frequency‑Aware Dual‑Domain Calibration module to consolidate low‑frequency structural cues while preserving high‑frequency modality‑specific details, and a Discrepancy‑Guided Spectral Modulation module that adaptively enhances, preserves, or attenuates the joint spectrum based on confidence‑weighted amplitude discrepancies. Experiments on DroneVehicle, ESCVehicle, and ATR‑UMOD show FoCal achieving high mAP scores (83.5%, 54.8%, 64.6%) with only 3.0 M parameters and 113.6 FPS, demonstrating a strong accuracy–efficiency trade‑off.
By Ben Liang, Chao Sui, Junqi Bai, Yuan Liu, Chunlai Li, Xiubao Sui, Qian Chen
arXiv:2606. 23702v1 Announce Type: cross Abstract: Modulation recognition systems rely on heterogeneous signal representations.
By Ronglai Qian, Liang An, Xiaoyan Wang, Qing Fan, Ziwei Huang, Yang Ye
arXiv:2606. 05754v1 Announce Type: cross Abstract: Phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) is widely used in large-scale distributed acoustic sensing (DAS) because it provides distributed spatiotemporal monitoring over long sensing distances.
By Weiguang Wang, Fugen Wu, Hailing Wang, Xuechen Liang, Xiaobin Li, Ru Han, Tianchang Xie