When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images.
Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness.
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms.
A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy.
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
Ranked #2 on Hand Gesture Recognition on NVGesture
We model the selection as an energy minimization problem with unary and pairwise potential functions.
Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks.
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics.
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm.
Data coding as a building block of several image processing algorithms has been received great attention recently.
Exploiting the local similarity of a descriptor and its nearby bases, a global measure of association of a descriptor to all the bases is computed.