1 code implementation • 14 Jul 2020 • Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger
Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w. r. t.
Ranked #3 on Multi-target Domain Adaptation on Office-31
1 code implementation • 13 Apr 2021 • Le Thanh Nguyen-Meidine, Madhu Kiran, Marco Pedersoli, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger
Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions.
1 code implementation • 20 Jun 2019 • Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin, Marco Pedersoli
Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks, the growing computational complexity and energy con-sumption of networks remains an issue, especially for ap-plications on platforms with limited resources and requir-ing real-time processing.
2 code implementations • 16 May 2020 • Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin
In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.
1 code implementation • 13 Apr 2021 • Madhu Kiran, R Gnana Praveen, Le Thanh Nguyen-Meidine, Soufiane Belharbi, Louis-Antoine Blais-Morin, Eric Granger
Hence, our proposed student-teacher framework is trained to address the occlusion problem by matching the distributions of between- and within-class distances (DCDs) of occluded samples with that of holistic (non-occluded) samples, thereby using the latter as a soft labeled reference to learn well separated DCDs.
no code implementations • 10 Sep 2018 • Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin
Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds.
no code implementations • 31 Oct 2019 • Madhu Kiran, Vivek Tiwari, Le Thanh Nguyen-Meidine, Eric Granger
However, bounding boxes provided by a state-of-the-art detector are noisy, due to changes in appearance, background and occlusion, which can cause the tracker to drift.
no code implementations • 9 Aug 2020 • Madhu Kiran, Amran Bhuiyan, Louis-Antoine Blais-Morin, Mehrsan Javan, Ismail Ben Ayed, Eric Granger
Our Mutual Attention network relies on the joint spatial attention between image and optical flow features maps to activate a common set of salient features across them.
Optical Flow Estimation Video-Based Person Re-Identification
no code implementations • 18 Jan 2021 • Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger
Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets.
no code implementations • 21 Feb 2022 • Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael Menelau Oliveira E Cruz, Louis-Antoine Blais-Morin, Eric Granger
This paper proposes a model adaptation method for Siamese trackers that uses a generative model to produce a synthetic template from the object search regions of several previous frames, rather than directly using the tracker output.
no code implementations • 7 Mar 2022 • Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael Menelau Oliveira E Cruz, Louis-Antoine Blais-Morin, Eric Granger
Results indicate that integrating our proposed method into state-of-art adaptive Siamese trackers can increase the potential benefits of a template update strategy, and significantly improve performance.