In real-world applications of machine learning, reliable and safe systems must consider
measures of performance beyond standard test set accuracy. These other goals include out …

Deep models must learn robust and transferable representations in order to perform well on
new domains. While domain transfer methods (eg, domain adaptation, domain …

Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive and high …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Domain adaptation solves image classification problems in the target domain by taking
advantage of the labelled source data and unlabelled target data. Usually, the source and …

Fully test-time adaptation aims to adapt the network model based on sequential analysis of
input samples during the inference stage to address the cross-domain performance …

Object pose estimation is a core perception task that enables, for example, object grasping
and scene understanding. The widely available, inexpensive and high-resolution RGB …

Fully test-time adaptation aims to adapt a network model online based on sequential
analysis of input samples during the inference stage. We observe that, when applying a …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Transformer-based methods have achieved remarkable success in various machine
learning tasks. How to design efficient test-time adaptation methods for transformer models …