24 citations · 118 across the 50 of their papers we have counts for
7 papers · 1 filter
Uncertainty-Guided Inference-Time Depth Adaptation for Transformer-Based Visual Tracking
Patrick Poggi, Divake Kumar, Theja Tulabandhula +1
Transformer-based single-object trackers achieve state-of-the-art accuracy but rely on fixed-depth inference, executing the full encoder--decoder stack for every frame regardless o…
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach
Nastaran Darabi, Dinithi Jayasuriya, Devashri Naik +2
Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, t…
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing
Sina Tayebati, Theja Tulabandhula, Amit R. Trivedi
In this work, we propose a disruptively frugal LiDAR perception dataflow that generates rather than senses parts of the environment that are either predictable based on the extensi…
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
Son The Nguyen, Theja Tulabandhula, Duy Nguyen
We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy. Dynamic Tiling starts…
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge
Alex C. Stutts, Danilo Erricolo, Sathya Ravi +2
In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance. Three-dimensional (3D) object detection, a critical rob…
Lightweight, Uncertainty-Aware Conformalized Visual Odometry
Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula +1
Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex e…
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
Priyesh Shukla, Sureshkumar S., Alex C. Stutts +3
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal f…