Thus, modeling and coordinating multimodal information is of great value for large-scale models to reason about real-world scenarios.

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

Procedural activities are sequences of key-steps aimed at achieving specific goals. They are crucial to build intelligent agents able to assist users effectively.