activity
20202022
most citedP4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

34 citations · 73 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

11 papers · 1 filter

cs.CV20223 cited

Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild

Jiayi Chen, Mi Yan, Jiazhao Zhang +6

In this work, we tackle the challenging task of jointly tracking hand object pose and reconstructing their shapes from depth point cloud sequences in the wild, given the initial po…

cs.CV20222 cited

Multi-Robot Active Mapping via Neural Bipartite Graph Matching

Kai Ye, Siyan Dong, Qingnan Fan +5

We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimati…

cs.CV2022

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

Tianchen Zhao, Niansong Zhang, Xuefei Ning +3

Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on…

cs.CV202111 cited

Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation

Xiaolong Li, Yijia Weng, Li Yi +4

Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models. To reduce the hu…

cs.CV2021

Contrastive Multimodal Fusion with TupleInfoNCE

Yunze Liu, Qingnan Fan, Shanghang Zhang +3

This paper proposes a method for representation learning of multimodal data using contrastive losses. A traditional approach is to contrast different modalities to learn the inform…

cs.CV202034 cited

P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

Yunze Liu, Li Yi, Shanghang Zhang +3

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used a…