Search Results for author:
Found 8 papers, 5 papers with code
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation
We begin with a simple premise: pose estimators often predict incoherent poses for out-of-distribution samples.
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that makes use of pseudo-labels generated from the target domain by the teacher network.
Ranked #1 on
Unsupervised Domain Adaptation
on GTA5-to-Cityscapes
A Mathematical Analysis of Learning Loss for Active Learning in Regression
We show that LearningLoss++ outperforms in identifying scenarios where the model is likely to perform poorly, which on model refinement translates into reliable performance in the open world.
ProtoGAN: Towards Few Shot Learning for Action Recognition
To the best of our knowledge, we are the first to report the results for G-FSL and provide a strong benchmark for future research.
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category.
Action Recognition In Videos
Out-of-Distribution Detection
+2
An Improved Learning Framework for Covariant Local Feature Detection
However, just learning from covariant constraint can lead to detection of unstable features.
A Large Dataset for Improving Patch Matching
Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.
Improved Descriptors for Patch Matching and Reconstruction
Scenes from the Oxford ACRD, MVS and Synthetic datasets are used for evaluating the patch matching performance of the learnt descriptors while the Strecha dataset is used to evaluate the 3D reconstruction task.
