Search Results for author:
Found 6 papers, 3 papers with code
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering
We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly.
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.
WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic
Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i. e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework.
Social and Information Networks Computers and Society
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering
Specifically, we investigate the use of scene graphs derived from images for Visual QA: an image is abstractly represented by a graph with nodes corresponding to object entities and edges to object relationships.
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive.
