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cs.CV2026
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…
cs.CV2025
Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation
Divake Kumar, Patrick Poggi, Sina Tayebati +3
Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce U…
cs.CV2025
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy
Nastaran Darabi, Divake Kumar, Sina Tayebati +1
In this work, we present INTACT, a novel two-phase framework designed to enhance the robustness of deep neural networks (DNNs) against noisy LiDAR data in safety-critical perceptio…