Search Results for author:
Found 9 papers, 3 papers with code
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives.
Proposal-Contrastive Pretraining for Object Detection from Fewer Data
However, for unsupervised pretraining, the popular contrastive learning requires a large batch size and, therefore, a lot of resources.
Spatio-temporal predictive tasks for abnormal event detection in videos
Abnormal event detection in videos is a challenging problem, partly due to the multiplicity of abnormal patterns and the lack of their corresponding annotations.
Ranked #2 on
Anomaly Detection
on UCSD Ped2
Object-centric and memory-guided normality reconstruction for video anomaly detection
This paper addresses video anomaly detection problem for videosurveillance.
Ranked #3 on
Anomaly Detection
on UCSD Peds2
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification
(ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance.
Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters
However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering.
Optimal Transport as a Defense Against Adversarial Attacks
This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks.
Improving Few-Shot Learning through Multi-task Representation Learning Theory
In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task.
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.
