no code implementations • ECCV 2020 • Poojan Oza, Vishal M. Patel
For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness.
no code implementations • ECCV 2020 • Poojan Oza, Hien V. Nguyen, Vishal M. Patel
To this end, we consider the problem of multiple class novelty detection under dataset distribution shift to improve the novelty detection performance.
1 code implementation • CVPR 2023 • Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo, Vishal M. Patel
Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data.
2 code implementations • 11 Apr 2022 • Vibashan VS, Poojan Oza, Vishal M. Patel
To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection.
1 code implementation • CVPR 2023 • Vibashan VS, Poojan Oza, Vishal M. Patel
The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
1 code implementation • 25 Aug 2021 • Shao-Yuan Lo, Poojan Oza, Vishal M. Patel
To this end, we propose a defense strategy that manipulates the latent space of novelty detectors to improve the robustness against adversarial examples.
1 code implementation • 19 Jul 2021 • Vibashan VS, Jeya Maria Jose Valanarasu, Poojan Oza, Vishal M. Patel
Furthermore, we show the effectiveness of the proposed ST fusion strategy with an ablation analysis.
no code implementations • 27 May 2021 • Poojan Oza, Vishwanath A. Sindagi, Vibashan VS, Vishal M. Patel
Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection.
no code implementations • 14 Apr 2021 • Poojan Oza, Vishal M. Patel
Using FL/SL frameworks, we can alleviate the lack of negative data problem by training a user authentication model over multiple user data distributed across devices.
no code implementations • CVPR 2021 • Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, Vishal M. Patel
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training.
2 code implementations • 21 Feb 2021 • Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, Vishal M. Patel
The proposed Medical Transformer (MedT) is evaluated on three different medical image segmentation datasets and it is shown that it achieves better performance than the convolutional and other related transformer-based architectures.
Ranked #1 on Medical Image Segmentation on Brain US
no code implementations • 8 Jan 2021 • Pramuditha Perera, Poojan Oza, Vishal M. Patel
One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class.
1 code implementation • 11 Jul 2020 • Yashasvi Baweja, Poojan Oza, Pramuditha Perera, Vishal M. Patel
Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users.
no code implementations • ECCV 2020 • Vishwanath A. Sindagi, Poojan Oza, Rajeev Yasarla, Vishal M. Patel
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images.
no code implementations • CVPR 2019 • Poojan Oza, Vishal M. Patel
It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes.
no code implementations • 7 Mar 2019 • Poojan Oza, Vishal M. Patel
We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition.
no code implementations • 4 Mar 2019 • Poojan Oza, Vishal M. Patel
Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users.
4 code implementations • 24 Jan 2019 • Poojan Oza, Vishal M. Patel
We present a novel Convolutional Neural Network (CNN) based approach for one class classification.