Search Results for author:
Found 9 papers, 7 papers with code
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.
Condition-Invariant Semantic Segmentation
Motivated by these findings, we propose to leverage stylization in performing feature-level adaptation by aligning the internal network features extracted by the encoder of the network from the original and the stylized view of each input image with a novel feature invariance loss.
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain.
Ranked #1 on
Source-Free Domain Adaptation
on Cityscapes to ACDC
Composite Learning for Robust and Effective Dense Predictions
In this paper, we find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
Ranked #102 on
Semantic Segmentation
on NYU Depth v2
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images.
Ranked #1 on
Semantic Segmentation
on Dark Zurich
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research.
Exploring Relational Context for Multi-Task Dense Prediction
Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.
Ranked #78 on
Semantic Segmentation
on NYU Depth v2
Automated Search for Resource-Efficient Branched Multi-Task Networks
The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently.
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning).
