In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …

Handling entirely unknown data is a challenge for any deployed classifier. Classification
models are typically trained on a static pre-defined dataset and are kept in the dark for the …

Triggered by the success of transformers in various visual tasks, the spatial self-attention
mechanism has recently attracted more and more attention in the computer vision …

Open-set image recognition (OSR) aims to both classify known-class samples and identify
unknown-class samples in the testing set, which supports robust classifiers in many realistic …

Semantically coherent out-of-distribution detection (SCOOD) is a recently proposed realistic
OOD detection setting: given labeled in-distribution (ID) data and mixed in-distribution and …

Open-vocabulary object detection (OVOD) aims to detect the objects beyond the set of
classes observed during training. This work introduces a straightforward and efficient …

Out-of-distribution (OOD) detection is essential when deploying neural networks in the real
world. One main challenge is that neural networks often make overconfident predictions on …

Trusted open set recognition aims to classify known classes and reject unknown ones, as
well as outputs an uncertainty estimate to measure the reliability of recognition results, thus …

Domain adaptation mitigates the decline in performance that occurs when models are
utilized in a target domain. Models designed for a limited range of categories struggle to …

Open set recognition (OSR) for medical images is vital to ensure practical safeguards in
clinical applications. It demands establishing model awareness of rare and unknown …