Search Results for author:
Found 7 papers, 6 papers with code
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains.
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Given the use of prototypes, the number of parameters required for our PMT method does not increase significantly with the number of source domains, thus reducing memory issues and possible overfitting.
Multi-Source Unsupervised Domain Adaptation
object-detection
+2
Density Crop-guided Semi-supervised Object Detection in Aerial Images
One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image.
Ranked #1 on
Object Detection
on VisDrone - 10% labeled data
Cascaded Zoom-in Detector for High Resolution Aerial Images
Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution images.
Ranked #1 on
2D Object Detection
on VisDrone
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes
Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models.
Unsupervised MKL in Multi-layer Kernel Machines
Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y. Cho and L. K.
Convolutional STN for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance.
