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Found 7 papers, 2 papers with code
From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs
In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs.
Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving
Large vision-language models (VLMs) have garnered increasing interest in autonomous driving areas, due to their advanced capabilities in complex reasoning tasks essential for highly autonomous vehicle behavior.
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges.
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
Recently, polar-based representation has shown promising properties in perceptual tasks.
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection
LiDAR and camera are two common sensors to collect data in time for 3D object detection under the autonomous driving context.
Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training
First, 3D-CoCo is inspired by our observation that the bird-eye-view (BEV) features are more transferable than low-level geometry features.
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
On one hand, PointAugmenting decorates point clouds with corresponding point-wise CNN features extracted by pretrained 2D detection models, and then performs 3D object detection over the decorated point clouds.
