A small set of seed samples (32 in our case) are automatically discovered by analyzing the temporal changes, which are manually labeled to train a segmentation and representation learning module.
The concept of geo-localization refers to the process of determining where on earth some `entity' is located, typically using Global Positioning System (GPS) coordinates.
We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications.
Deep neural networks have shown promising results in disease detection and classification using medical image data.
We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift.
The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction.
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging.
Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malaria.
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
Temporal localization (i. e. indicating the start and end frames of the action in a video) is referred to as frame-level detection.
Temporal localization (i. e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.
Natural and man-made disasters cause huge damage to built infrastructures and results in loss of human lives.
However, using deep neural networks for automatic aerial action recognition is difficult due to the need for a large number of training aerial human action videos.
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Ranked #2 on Abnormal Event Detection In Video on UBI-Fights
Given the action proposals in a video, the goal of the proposed work is to generate a few better action proposals that are ranked properly.
%We reconstruct video action proposals from image action proposals while enforcing consistency across coefficient vectors of multiple frames by consensus regularization.
This paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training.
Ranked #2 on Domain Adaptation on UCF-to-Olympic