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Found 7 papers, 2 papers with code
Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes
Previous methods for finding sparsity rates mainly focus on the training-aware scenario, which usually fails to converge stably under the PTS setting with limited data and much less training cost.
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru
We develop a custom, state-of-the-art emotion recognition model to dynamically select the robot's tone of voice and utilize emojis from LLM output as cues for generating robot actions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction.
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.
Ranked #24 on
3D Semantic Segmentation
on SemanticKITTI
Inferring Spatial Uncertainty in Object Detection
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
The fusion method shows particular benefit for detection of pedestrians in the bird view compared to other fusion-based object detection networks.
