To addresses this degeneracy issue, we present a simple yet effective device, Zipper.

Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (V AD) for robotic power line inspection.

The core idea is that a member sample exhibits more resistance to adversarial perturbations than a non-member.

Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy. We present TransCLIP, a novel and computationally efficient transductive approach designed for Vision-Language Models (VLMs).

We also describe how to use predictors (i.e., the

This document provides additional details that complement the main paper. We discuss the steps used to synchronize and calibrate the visual data in Section A. Section B elaborates on the details of UWB localization, heading direction estimation, and obtaining the reference for lying behavior. We keep the order of figures, tables, and equations in numerical, and refer to them independently from the main paper unless explicitly stated otherwise. The paper checklist is attached as the final part of the main paper. We discuss additional details of processing the visual data and calibrating four camera views.