Search Results for author:
Found 13 papers, 7 papers with code
Stereographic Spherical Sliced Wasserstein Distances
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning.
Partial Transport for Point-Cloud Registration
In this paper, we approach the point-cloud registration problem through the lens of optimal transport theory and first propose a comprehensive set of non-rigid registration methods based on the optimal partial transportation problem.
PT$\mathrm{L}^{p}$: Partial Transport $\mathrm{L}^{p}$ Distances
In this paper, we introduce partial transport $\mathrm{L}^{p}$ distances as a new family of metrics for comparing generic signals, benefiting from the robustness of partial transport distances.
A Simplified Framework for Contrastive Learning for Node Representations
Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others.
Is Multi-Task Learning an Upper Bound for Continual Learning?
Moreover, it is imaginable that when learning from multiple tasks, a small subset of these tasks could behave as adversarial tasks reducing the overall learning performance in a multi-task setting.
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment.
Fine-Grained Visual Classification using Self Assessment Classifier
We show that by effectively addressing the ambiguity in the top-k prediction classes, our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
Ranked #4 on
Fine-Grained Image Classification
on Stanford Dogs
WayFAST: Navigation with Predictive Traversability in the Field
We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate.
Coarse-to-Fine Reasoning for Visual Question Answering
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task.
Ranked #1 on
Visual Question Answering (VQA)
on GQA test-dev
Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge
We design the student network such as it is light-weight and well suitable for deployment on a typical CPU.
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering
Different approaches have been proposed to Visual Question Answering (VQA).
Compact Trilinear Interaction for Visual Question Answering
In Visual Question Answering (VQA), answers have a great correlation with question meaning and visual contents.
Ranked #2 on
Visual Question Answering (VQA)
on TDIUC
