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Found 25 papers, 7 papers with code
Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery
In this work, we propose a topology-aware unsupervised domain adaptation approach for road segmentation in remote sensing imagery.
GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set.
Cross-View Image Sequence Geo-localization
In this paper, we present the first cross-view geo-localization method that works on a sequence of limited FOV images.
TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos
Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones.
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach
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.
Visual and Object Geo-localization: A Comprehensive Survey
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.
Towards Low-Cost and Efficient Malaria Detection
We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications.
Out of distribution detection for skin and malaria images
Deep neural networks have shown promising results in disease detection and classification using medical image data.
Cross-Region Building Counting in Satellite Imagery using Counting Consistency
We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift.
Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling
The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction.
Dogfight: Detecting Drones from Drones Videos
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging.
A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images
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.
Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks
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.
Action recognition in real-world videos
Temporal localization (i. e. indicating the start and end frames of the action in a video) is referred to as frame-level detection.
Video Anomaly Detection for Smart Surveillance
Temporal localization (i. e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.
Destruction from sky: Weakly supervised approach for destruction detection in satellite imagery
Natural and man-made disasters cause huge damage to built infrastructures and results in loss of human lives.
Human Action Recognition in Drone Videos using a Few Aerial Training Examples
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.
Deep Built-Structure Counting in Satellite Imagery Using Attention Based Re-Weighting
In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery.
Real-world Anomaly Detection in Surveillance 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
Activity Recognition
Anomaly Detection In Surveillance Videos
+2
Unsupervised Action Proposal Ranking through Proposal Recombination
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.
What If We Do Not Have Multiple Videos of the Same Action? -- Video Action Localization Using Web Images
%We reconstruct video action proposals from image action proposals while enforcing consistency across coefficient vectors of multiple frames by consensus regularization.
Optical Flow Estimation
Spatio-Temporal Action Localization
+1
Automatic Action Annotation in Weakly Labeled Videos
The output of our method is the most action representative proposals from each video.
Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition
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
