19 citations · 57 across the 9 of their papers we have counts for
9 papers · 1 filter
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
Runnan Chen, Xinge Zhu, Nenglun Chen +6
Current successful methods of 3D scene perception rely on the large-scale annotated point cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a…
Self-Supervised Image Representation Learning with Geometric Set Consistency
Nenglun Chen, Lei Chu, Hao Pan +2
We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency. Our intuition is that 3D geometric consistency priors such as…
Towards 3D Scene Understanding by Referring Synthetic Models
Runnan Chen, Xinge Zhu, Nenglun Chen +5
Promising performance has been achieved for visual perception on the point cloud. However, the current methods typically rely on labour-extensive annotations on the scene scans. In…
PR-Net: Preference Reasoning for Personalized Video Highlight Detection
Runnan Chen, Penghao Zhou, Wenzhe Wang +4
Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention. Cu…
Distributed Attention for Grounded Image Captioning
Nenglun Chen, Xingjia Pan, Runnan Chen +7
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image w…
Point2Skeleton: Learning Skeletal Representations from Point Clouds
Cheng Lin, Changjian Li, Yuan Liu +3
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stri…