South America
Active 3D Shape Reconstruction from Vision and Touch Edward J. Smith 1,2 David Meger 2 Luis Pineda 1 Roberto Calandra 1
Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.
Appendix GOCor: BringingGloballyOptimized CorrespondenceVolumesintoYourNeuralNetwork
The Dynamic dataset allows the network to learn the presence of independently moving objects and motionboundaries.
Two-StreamNetworkforSignLanguageRecognition andTranslation
Weadoptidentical dataaugmentationsforRGBvideos andheatmap sequences to maintain spatial and temporal consistency. SingleStream-SLTwhich only utilizes asingle video encoder without modelling keypoints serves as our baseline. TwoStream-SLT-V/K/J denotes the network where only one translation network is attached onto the video head/keypoint head/joint head. The averaged probabilities are used to decode text sequences. In each of the variants, only a single translation network is appended onto the video head, keypoint head, or joint head.
A Influence Function on Bias and Extension to
For real-world visual datasets (like facial dataset or ImageNet), the unavailability of strict counterfactual data is a common challenge.
Fast Model Debias with Machine Unlearning
Such error correlations not only affect the model's ability to make robust predictions but also
