no code implementations • 18 Apr 2024 • Sai Sree Harsha, Ambareesh Revanur, Dhwanit Agarwal, Shradha Agrawal
Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit using our novel target and shape aware InvEdit masks.
no code implementations • CVPR 2023 • Ambareesh Revanur, Debraj Basu, Shradha Agrawal, Dhwanit Agarwal, Deepak Pai
We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity.
1 code implementation • 24 Feb 2022 • Ambareesh Revanur, Ananyananda Dasari, Conrad S. Tucker, Laszlo A. Jeni
It outperformed both shallow and deep learning based methods for instantaneous respiration rate estimation.
1 code implementation • 22 Sep 2021 • Ambareesh Revanur, Zhihua Li, Umur A. Ciftci, Lijun Yin, Laszlo A. Jeni
Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic.
no code implementations • 16 Sep 2021 • Ambareesh Revanur, Vijay Kumar, Deepthi Sharma
We consider the problem of complementary fashion prediction.
Ranked #2 on Recommendation Systems on Polyvore
no code implementations • NeurIPS 2020 • Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, R. Venkatesh Babu
Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation.
Domain Adaptation Multi-Source Unsupervised Domain Adaptation
no code implementations • ECCV 2020 • Jogendra Nath Kundu, Rahul Mysore Venkatesh, Naveen Venkat, Ambareesh Revanur, R. Venkatesh Babu
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA).
1 code implementation • ECCV 2020 • Jogendra Nath Kundu, Ambareesh Revanur, Govind Vitthal Waghmare, Rahul Mysore Venkatesh, R. Venkatesh Babu
Our approach not only generalizes to in-the-wild images, but also yields a superior trade-off between speed and performance, compared to prior top-down approaches.
1 code implementation • CVPR 2020 • Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M. V, R. Venkatesh Babu
Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future.