1 code implementation • CVPR 2024 • K L Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash
We show that such models can be vulnerable to a universal adversarial patch attack where the attacker optimizes for a patch that when pasted on any image can increase the compute and power consumption of the model.
1 code implementation • 5 Dec 2023 • Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet, Hamed Pirsiavash, Hadi Jamali-Rad
To achieve this, we adjust the noise level (equivalently, number of diffusion iterations) to ensure the generated image retains low-level and background features from the source image while representing the target category, resulting in a hard negative sample for the source category.
1 code implementation • 13 Jan 2022 • K L Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Feature regression is a simple way to distill large neural network models to smaller ones.
1 code implementation • CVPR 2020 • K L Navaneet, Ansu Mathew, Shashank Kashyap, Wei-Chih Hung, Varun Jampani, R. Venkatesh Babu
We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision.
3D Object Reconstruction From A Single Image
3D Point Cloud Reconstruction
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1 code implementation • 20 Jul 2018 • Priyanka Mandikal, K L Navaneet, Mayank Agarwal, R. Venkatesh Babu
3D reconstruction from single view images is an ill-posed problem.
no code implementations • 19 Jul 2018 • K L Navaneet, Ravi Kiran Sarvadevabhatla, Shashank Shekhar, R. Venkatesh Babu, Anirban Chakraborty
Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras.