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Found 10 papers, 5 papers with code
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes.
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection
LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals.
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Large Language Models exhibit robust problem-solving capabilities for diverse tasks.
Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
To overcome the bottleneck, we propose the framework of model broadcasting and assembling (MBA), which represents the first attempt on leveraging reusable knowledge, referring to shared parameters among tasks, to enable parameter broadcasting to reduce communication overhead.
Virtual Sparse Convolution for Multimodal 3D Object Detection
Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework.
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations
Combining CoIn with an iterative training strategy, we propose a CoIn++ pipeline, which requires only 2% annotations in the KITTI dataset to achieve performance comparable to the fully supervised methods.
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.
Transformation-Equivariant 3D Object Detection for Autonomous Driving
However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed.
In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
