no code implementations • 16 May 2024 • Anish Bhattacharya, Nishanth Rao, Dhruv Parikh, Pratik Kunapuli, Nikolai Matni, Vijay Kumar
We demonstrate the capabilities of an attention-based end-to-end approach for high-speed quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art architectures.
no code implementations • 6 Apr 2024 • Sachini Wickramasinghe, Dhruv Parikh, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna, Carl Busart
We directly train this model on SAR datasets which have limited training samples to evaluate its effectiveness for SAR ATR applications.
no code implementations • 21 Mar 2024 • Dhruv Parikh, Shouyi Li, Bingyi Zhang, Rajgopal Kannan, Carl Busart, Viktor Prasanna
For algorithm design, we systematically combine a hardware-aware structured block-pruning method for pruning model parameters and a dynamic token pruning method for removing unimportant token vectors.