216 citations · 508 across the 15 of their papers we have counts for
8 papers · 1 filter
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers
Junting Chen, Guohao Li, Suryansh Kumar +2
Object goal navigation is an important problem in Embodied AI that involves guiding the agent to navigate to an instance of the object category in an unknown environment -- typical…
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Ling Yang, Zhilin Huang, Yang Song +6
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and the…
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Zhen Dong, Kaicheng Zhou, Guohao Li +5
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs). However, the best way to use data to search network architectures i…
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
Bing Li, Cheng Zheng, Guohao Li +1
We propose a novel scene flow method that captures 3D motions from point clouds without relying on ground-truth scene flow annotations. Due to the irregularity and sparsity of poin…
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
Guocheng Qian, Abdulellah Abualshour, Guohao Li +2
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module,…
DeepGCNs: Making GCNs Go as Deep as CNNs
Guohao Li, Matthias Müller, Guocheng Qian +4
Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, act…
DeepGCNs: Can GCNs Go as Deep as CNNs?
Guohao Li, Matthias Müller, Ali Thabet +1
Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to…
OIL: Observational Imitation Learning
Guohao Li, Matthias Müller, Vincent Casser +3
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images. However, t…