The challenge of adapting an anomaly detector to drift in the normal data distribution, especially when no training data is available for the "new normal", has

An important step to understanding and improving artificial vision systems is to measure image similarity purely based on intrinsic object properties that define object identity. This problem has been studied in the computer vision literature as re-identification, though mostly restricted to specific object categories such as people and cars. We propose to extend it to general object categories, exploring an image similarity metric based on object intrinsics.

For instance, data augmentation consistency regularization [Sohn et al., 2020, Berthelot et al., 2019] generates pseudo-labels from the current model predictions on

By analyzing the unique properties and behaviors of backdoor attacks, we formulate trigger restoration as an optimization problem and design a novel metric to detect back-doored policies.

SiamMAE operates on pairs of randomly sampled video frames and asymmetrically masks them.

A core challenge for deep learning approaches on CO is the lack of training data.

Video highlight detection is a task to automatically select the most engaging moments from a long video.