Search Results for author:
Found 5 papers, 1 papers with code
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
Active learning using weakly supervised signals for quality inspection
In this work, we develop a methodology for learning actively, from rapidly mined, weakly (i. e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives.
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation
Generalization Performance of Deep Learning models trained using Empirical Risk Minimization can be improved significantly by using Data Augmentation strategies such as simple transformations, or using Mixed Samples.
Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework
To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data.
Unsupervised Anomalous Trajectory Detection for Crowded Scenes
The proposed work is based on four major steps, namely, extraction of trajectories from crowded scene video, extraction of several features from these trajectories, independent mean-shift clustering and anomaly detection.
