arXiv subjects

Elif Ozden-Yenigun

Publications and source records attributed to Elif Ozden-Yenigun.

3 recordsLinked to original sources

arXiv2026

Cost-Aware Vision--Language Model Arbitration for Fabric Structure Recognition A Deployable Multi-Agent System

Recognizing a fabric's structure is a prerequisite for translating textile-specific material information into structured digital form for downstream supply-chain systems. Pure CNN classifiers are cost-efficient but fail on visually ambiguous categories; vision--language models (VLMs) generalize more broadly but cost much more per image and are unstable on specialist domains. We present a multi-agent system in which a CNN cascade handles the easy majority and a VLM is invoked only as a selective arbiter, constrained to a top-3 taxonomy-consistent choice. The fabric taxonomy performs as a constraint for the whole recognition process to increase the accuracy and reduce the VLM calls. Meanwhile, the CNN cascade is distilled to a small parameter size to reduce the inference time and meet the needs of practical deployment. On a newly curated 14-class benchmark, a flat ConvNeXt-Tiny baseline reaches $90.45\,\%$ top-1 and $76.9\,\%$ on the four hardest classes; \method's hierarchical cascade reaches $93.94\,\%$ top-1 and $94.50\,\%$ hard ($+17.6$\,pp). Tightening the VLM trigger from $60\,\%$ to $<\!10\,\%$ cuts API cost by ${\sim}90\,\%$ with no measurable accuracy loss. CPU inference is $\le\!93$\,ms without a VLM call ($9.3$\,ms distilled). Each prediction carries a machine-readable reasoning record, offered as an entry point for future supply-chain documentation.

eess.IV↗

arXiv2026

MSTF-Net: A UAV-Oriented Multi-Spectral Video Segmentation Method via Modality-Robust, Scale-Adaptive, and Consistent Fusion

Multi-spectral video segmentation is essential for robust scene understanding in unmanned aerial vehicle (UAV) applications such as city planning, land use monitoring, traffic monitoring, and crowd estimation. While the fusion of RGB and thermal modalities offers complementary information for perception under varying lighting and visibility conditions, two fundamental challenges remain: (1) the modal fusion dilemma, arising from significant discrepancies between RGB and thermal features that obscure complementary cues, and (2) temporal variation, induced by rapid motion and viewpoint changes on UAV platforms, which leads to appearance inconsistency and misalignment across frames. To address these issues, this study proposed MSTF-Net, a modality-robust scale-adaptive fusion framework for multi-spectral video segmentation that effectively models cross-modal fusion and temporal consistency. The Modality Spatial Complementary Suppression and Enhancement (MSCSE) module generates unified instance queries via cross-modal attention and suppresses modality-specific noise using residual-guided discrepancy filtering and consistency constraints. To model temporal dynamics, the Multi-scale Temporal Cross-modality Semantic Consistency (MTCSC) module adaptively adjusts the temporal receptive field based on frame distance, capturing both coarse global context and fine local structure across time. Extensive ablation experiments on public RGB-T datasets demonstrate that MSTF-Net achieves state-of-the-art segmentation performance, especially under challenging conditions such as small targets, occlusion, and modality degradation. Specifically, reached 56.42\% mIoU on the MVSeg dataset and 51.80\% mIoU on the CART dataset, respectively.

eess.IV↗

arXiv2020

Graphene-based Soft Wearable Antennas

Electronic textiles (e-textiles) are about to face tremendous environmental and resource challenges due to the complexity of sorting, the risk to supplies and metal contamination in textile recycling streams. This is because e-textiles are heavily based on the integration of valuable metals, including gold, silver and copper. In the context of exploring sustainable materials in e-textiles, we tested the boundaries of multi-layer (ML) graphene in wearable communication applications, in which metal assemblies are leading the way in wearable communication. This study attempts to create a soft, textile-based communication interface that does not disrupt tactile comfort and conformity by introducing ML graphene sheets. The antenna design proposed is based on a multidisciplinary approach that merges electromagnetic engineering and material science and integrates graphene, a long-lasting alternative to metal components. The designed antenna covers a wide bandwidth ranging from 3 GHz to 9 GHz, which is a promising solution for a high data rate and efficient communication link. We also described the effects of bending and proximity to the human body on the antenna performance. Overall, the results suggested that graphene-based soft antennas are a viable solution for a fully integrated textile-based communication interface that can replace the current rigid, restrictive and toxic approaches, leading to a future where eco-friendliness and sustainability is the only way forward!

physics.app-ph↗