no code implementations • 2 Nov 2023 • Trevor Ablett, Oliver Limoyo, Adam Sigal, Affan Jilani, Jonathan Kelly, Kaleem Siddiqi, Francois Hogan, Gregory Dudek
An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor.
no code implementations • 20 Jun 2023 • Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh
We approach this problem from the lens of Koopman theory, where the nonlinear dynamics of the environment can be linearized in a high-dimensional latent space.
no code implementations • 31 Mar 2023 • Mohammad Khodadad, Morteza Rezanejad, Ali Shiraee Kasmaee, Kaleem Siddiqi, Dirk Walther, Hamidreza Mahyar
To address these limitations we introduce a novel Multi-level Graph Convolution Neural (MLGCN) model, which uses Graph Neural Networks (GNN) blocks to extract features from 3D point clouds at specific locality levels.
no code implementations • 27 Nov 2021 • Morteza Rezanejad, Babak Samari, Elham Karimi, Ioannis Rekleitis, Gregory Dudek, Kaleem Siddiqi
In topology matching between two given maps and their AOF skeletons, we first find correspondences between points on the AOF skeletons of two different environments.
no code implementations • CVPR 2022 • Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar, Herve Lombaert, Michael Gruninger, Dirk B. Walther, Kaleem Siddiqi
In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds.
no code implementations • 29 Sep 2021 • Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh
We study different notions of equivariance as an inductive bias in Reinforcement Learning (RL) and propose new mechanisms for recovering representations that are equivariant to both an agent’s action, and symmetry transformations of the state-action pairs.
no code implementations • 22 Apr 2021 • Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi
Current deep learning models for classification tasks in computer vision are trained using mini-batches.
1 code implementation • 1 Jul 2020 • Arnab Kumar Mondal, Pratheeksha Nair, Kaleem Siddiqi
In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been successfully applied as function approximators in Deep Q-Learning algorithms, which seek to learn action-value functions and policies in various environments.
no code implementations • CVPR 2020 • Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas, Kaleem Siddiqi, Sven Dickinson
We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes.
no code implementations • CVPR 2020 • Chu Wang, Babak Samari, Vladimir G. Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
Affinity graphs are widely used in deep architectures, including graph convolutional neural networks and attention networks.
1 code implementation • 4 Jun 2019 • Chu Wang, Marcello Pelillo, Kaleem Siddiqi
We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets.
no code implementations • 27 May 2019 • Chu Wang, Babak Samari, Vladimir Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
Thus far the learning of attention weights has been driven solely by the minimization of task specific loss functions.
2 code implementations • CVPR 2019 • Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi
In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.
Ranked #1 on Object Skeleton Detection on SK-LARGE
no code implementations • CVPR 2019 • Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B. Walther, Allan Jepson, Sven Dickinson, Kaleem Siddiqi
That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.
no code implementations • 15 Apr 2018 • Kuldeep Kumar, Kaleem Siddiqi, Christian Desrosiers
Results highlight the ability of our method to group streamlines into plausible bundles and illustrate the impact of sparsity priors on the performance of the proposed methods.
1 code implementation • ECCV 2018 • Chu Wang, Babak Samari, Kaleem Siddiqi
In the present article, we propose to overcome this limitation by using spectral graph convolution on a local graph, combined with a novel graph pooling strategy.
Ranked #83 on 3D Point Cloud Classification on ModelNet40