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Found 13 papers, 3 papers with code
PW-Self: Patch-Wise Self-Supervised Visual Representation Learning
To this end, we present a simple yet effective patch-matching algorithm that can find the corresponding patches across the augmented views.
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers
In this work, by converting each user's interactions with items into a series of events and basing our architecture on Transformers, we try to enable the use of such a model that takes different types of events into account.
Updating Street Maps using Changes Detected in Satellite Imagery
To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps.
An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods
One of the main challenges in recommender systems is data sparsity which leads to high variance.
Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories
Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults.
Log representation as an interface for log processing applications
Log files are files that record events, messages, or transactions.
RoadTagger: Robust Road Attribute Inference with Graph Neural Networks
The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.
Inferring and Improving Street Maps with Data-Driven Automation
Through an evaluation on a large-scale dataset including satellite imagery, GPS trajectories, and ground-truth map data in forty cities, we show that Mapster makes automation practical for map editing, and enables the curation of map datasets that are more complete and up-to-date at less cost.
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed.
Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs
We conducted two sets of experiments based on two different protocols of OSV, i. e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG.
BlockCNN: A Deep Network for Artifact Removal and Image Compression
For artifact removal, we input a JPEG image and try to remove its compression artifacts.
Local Visual Microphones: Improved Sound Extraction from Silent Video
We show that different locations in the image vibrate differently.
Fast Template Evaluation with Vector Quantization
Our procedure allows speed and accuracy to be traded off in two ways: by choosing the number of Vector Quantization levels, and by choosing to rescore windows or not.
