Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Meethal, Akhil"'
The demand for cognitive load assessment with low-cost easy-to-use equipment is increasing, with applications ranging from safety-critical industries to entertainment. Though pupillometry is an attractive solution for cognitive load estimation in suc
Externí odkaz:
http://arxiv.org/abs/2409.03888
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 (MSDA) allows leveraging multiple annotated sourc
Externí odkaz:
http://arxiv.org/abs/2403.09918
Adapting visual object detectors to operational target domains is a challenging task, commonly achieved using unsupervised domain adaptation (UDA) methods. Recent studies have shown that when the labeled dataset comes from multiple source domains, tr
Externí odkaz:
http://arxiv.org/abs/2309.14950
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. This bottleneck is further exacerbated in aerial images where
Externí odkaz:
http://arxiv.org/abs/2308.05032
Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution images. Density cropping is a widely used method to improve this small object detection wher
Externí odkaz:
http://arxiv.org/abs/2303.08747
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. State-of-art approaches for semi
Externí odkaz:
http://arxiv.org/abs/2204.00147
Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y.Cho and L.K. Saul in \cite{saul}. In MKMs they used only one kernel(arc-cosine kernel) at a layer for the kernel PCA-based feature extraction. We propose to use mult
Externí odkaz:
http://arxiv.org/abs/2111.13769
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the architecture of a standard CNN by using the activation maps of the las
Externí odkaz:
http://arxiv.org/abs/1912.01522