34 citations · 48 across the 7 of their papers we have counts for
6 papers · 1 filter
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
Haoran Geng, Ziming Li, Yiran Geng +3
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categor…
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…
End-to-End Object Detection with Adaptive Clustering Transformer
Minghang Zheng, Peng Gao, Renrui Zhang +4
End-to-end Object Detection with Transformer (DETR)proposes to perform object detection with Transformer and achieve comparable performance with two-stage object detection like Fas…
Generative 3D Part Assembly via Dynamic Graph Learning
Jialei Huang, Guanqi Zhan, Qingnan Fan +5
Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a si…
Unpaired Image-to-Image Translation using Adversarial Consistency Loss
Yihao Zhao, Ruihai Wu, Hao Dong
Unpaired image-to-image translation is a class of vision problems whose goal is to find the mapping between different image domains using unpaired training data. Cycle-consistency…
DLGAN: Disentangling Label-Specific Fine-Grained Features for Image Manipulation
Guanqi Zhan, Yihao Zhao, Bingchan Zhao +3
Recent studies have shown how disentangling images into content and feature spaces can provide controllable image translation/ manipulation. In this paper, we propose a framework t…