no code implementations • 22 Oct 2022 • Kei Ota, Hsiao-Yu Tung, Kevin A. Smith, Anoop Cherian, Tim K. Marks, Alan Sullivan, Asako Kanezaki, Joshua B. Tenenbaum
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e. g., a door might open inwards or outwards.
no code implementations • ICCV 2021 • Anoop Cherian, Goncalo Dias Pais, Siddarth Jain, Tim K. Marks, Alan Sullivan
To use our model for instance segmentation, we propose an instance pose encoder that learns to take in a generated depth image and reproduce the pose code vectors for all of the object instances.
no code implementations • 14 Nov 2020 • Kei Ota, Devesh K. Jha, Diego Romeres, Jeroen van Baar, Kevin A. Smith, Takayuki Semitsu, Tomoaki Oiki, Alan Sullivan, Daniel Nikovski, Joshua B. Tenenbaum
The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.
no code implementations • 31 Aug 2019 • Xenju Xu, Guanghui Wang, Alan Sullivan, Ziming Zhang
In this paper we propose integrating a priori knowledge into both design and training of convolutional neural networks (CNNs) to learn object representations that are invariant to affine transformations (i. e., translation, scale, rotation).
no code implementations • 26 Mar 2019 • Esra Ataer-Cansizoglu, Michael Jones, Ziming Zhang, Alan Sullivan
Face super-resolution methods usually aim at producing visually appealing results rather than preserving distinctive features for further face identification.
no code implementations • 2 Mar 2019 • Ziming Zhang, Anil Kag, Alan Sullivan, Venkatesh Saligrama
We show that such self-feedback helps stabilize the hidden state transitions leading to fast convergence during training while efficiently learning discriminative latent features that result in state-of-the-art results on several benchmark datasets at test-time.
no code implementations • 2 Mar 2019 • Ziming Zhang, Wenju Xu, Alan Sullivan
In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization.
no code implementations • 13 Sep 2018 • Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh Jha, Diego Romeres, Daniel Nikovski
Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task---rather than being productive---can be reduced by transferring the learned task to the real robot.
1 code implementation • 12 Jul 2018 • Anoop Cherian, Alan Sullivan
To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm.
no code implementations • 22 May 2018 • Ziming Zhang, Rongmei Lin, Alan Sullivan
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition.