1 code implementation • 13 Jul 2021 • Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
no code implementations • 7 Apr 2021 • Antoine Cordier, Deepan Das, Pierre Gutierrez
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.
no code implementations • 6 Jun 2020 • Deepan Das, Haley Massa, Abhimanyu Kulkarni, Theodoros Rekatsinas
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.
no code implementations • 8 Aug 2019 • Deepan Das, Noor Mohammed Ghouse, Shashank Verma, Yin Li
To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data.
no code implementations • 3 Jul 2019 • Deepan Das, Deepak Mishra
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.