Author

M. Pesé

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Preprint Oct 2026

Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers

Accurate 3D semantic perception is critical for safe autonomous navigation. However, supervised LiDAR segmentation remains tied to closed taxonomies and to the cost of point-wise manual annotation. Open-vocabulary methods avoid that cost by projecting the output of 2D vision-language models onto LiDAR and distilling it...

Cigdem Kokenoz, Amir Salarpour, Alkim Domeke et al. · 0 citations

Sep 2026

Evaluating the Robustness of Segmentation Models against Adversarial Patches in Off-Road Environments

Semantic Segmentation (SS) is critical for autonomous vehicles to navigate off-road environments by identifying drivable terrain. Although models like ResNet34+UNet and EfficientViT have been proposed for these tasks, their susceptibility to localized adversarial patches in unstructured environments remains under-resea...

Christopher Salas, M. Pesé, Bing Li et al. · SAE technical paper series · 0 citations

Preprint Aug 2026

GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models

Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data that differ from the pretraining distribution. Test-time adaptation offers a practical way to improve robustness without source data or target labels, yet existing methods...

Pedram MohajerAnsari, Amir Salarpour, Run-Min Wang et al. · 0 citations

Open access Aug 2026

GuixChain: Enforcing Reproducible Builds and Provenance Integrity for Secure Automotive OTA Pipelines

Ensuring the integrity of automotive software, from source code to deployed binaries, has become critical as vehicles increasingly rely on over-the-air (OTA) updates and complex supply chains. The Uptane framework secures OTA update delivery for automotive systems but does not enforce integrity within the upstream soft...

Iwinosa Aideyan, M. Pesé, Richard. R. Brooks · Distributed Ledger Technolog... · 0 citations

Preprint Aug 2026

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles

Evaluated on GTSRB and LISA across four backbones and three physical attack types, LAMDA is the only method among ten evaluated that consistently improves robustness across all attack-backbone-dataset combinations, while preserving or improving clean accuracy in nearly all cases.

Pedram MohajerAnsari, Amir Salarpour, M. Pesé · 0 citations