32 citations · 82 across the 6 of their papers we have counts for
6 papers
Gaussian Building Mesh (GBM): Extract a Building's 3D Mesh with Google Earth and Gaussian Splatting
Kyle Gao, Liangzhi Li, Hongjie He +3
Recently released open-source pre-trained foundational image segmentation and object detection models (SAM2+GroundingDINO) allow for geometrically consistent segmentation of object…
Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processing
Dening Lu, Jun Zhou, Kyle +3
Recently, LiDAR point cloud processing and analysis have made great progress due to the development of 3D Transformers. However, existing 3D Transformer methods usually are computa…
Transformers in 3D Point Clouds: A Survey
Dening Lu, Qian Xie, Mingqiang Wei +3
Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring the use…
Climbing the WOL: Training for Cheaper Inference
Zichang Liu, Zhaozhuo Xu, Alan Ji +3
Efficient inference for wide output layers (WOLs) is an essential yet challenging task in large scale machine learning. Most approaches reduce this problem to approximate maximum i…
PipeMare: Asynchronous Pipeline Parallel DNN Training
Bowen Yang, Jian Zhang, Jonathan Li +3
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware u…
Squeeze-and-Attention Networks for Semantic Segmentation
Zilong Zhong, Zhong Qiu Lin, Rene Bidart +6
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. Howeve…