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Found 6 papers, 1 papers with code
Panoptic Out-of-Distribution Segmentation
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task.
Fairness and Bias in Robot Learning
In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments.
Ranked #1 on
Panoptic Segmentation
on Panoptic nuScenes test
There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge
In this work, we present the novel self-supervised MM-DistillNet framework consisting of multiple teachers that leverage diverse modalities including RGB, depth and thermal images, to simultaneously exploit complementary cues and distill knowledge into a single audio student network.
From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation
The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments.
MOPT: Multi-Object Panoptic Tracking
In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking.
