21 research outputs found
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
The aim of object-centric vision is to construct an explicit representation
of the objects in a scene. This representation is obtained via a set of
interchangeable modules called \emph{slots} or \emph{object files} that compete
for local patches of an image. The competition has a weak inductive bias to
preserve spatial continuity; consequently, one slot may claim patches scattered
diffusely throughout the image. In contrast, the inductive bias of human vision
is strong, to the degree that attention has classically been described with a
spotlight metaphor. We incorporate a spatial-locality prior into
state-of-the-art object-centric vision models and obtain significant
improvements in segmenting objects in both synthetic and real-world datasets.
Similar to human visual attention, the combination of image content and spatial
constraints yield robust unsupervised object-centric learning, including less
sensitivity to model hyperparameters.Comment: 16 pages, 3 figures, under review at NeurIPS 202