Search Results for author:
Found 7 papers, 0 papers with code
Continuously Reliable Detection of New-Normal Misinformation: Semantic Masking and Contrastive Smoothing in High-Density Latent Regions
Toxic misinformation campaigns have caused significant societal harm, e. g., affecting elections and COVID-19 information awareness.
Time-Aware Datasets are Adaptive Knowledgebases for the New Normal
Recent advances in text classification and knowledge capture in language models have relied on availability of large-scale text datasets.
ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature Extraction using Annotation Team of Experts
We evaluate ATEAM and KID for vehicle recognition problems and show that our integrated dataset can help off-the-shelf models achieve excellent accuracy on VMMR and vehicle re-id with no changes to model architectures.
EdnaML: A Declarative API and Framework for Reproducible Deep Learning
We present several examples of ML pipelines with EdnaML, including a large-scale fake news labeling and classification system with six sub-pipelines managed by EdnaML.
Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning
CoLabel performs VMMR with a composite of interpretable features such as vehicle color, type, and make, all based on interpretable annotations of the ground truth labels.
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach
We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexible vehicle re-id under a variety of compute environments such as cloud, mobile, edge, or embedded devices by only changing the re-id model backbone.
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks
Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known.
