940 citations · 2.5k across the 35 of their papers we have counts for
9 papers · 1 filter
iBOT: Image BERT Pre-Training with Online Tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang +4
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pie…
Self-Supervised Learning by Estimating Twin Class Distributions
Feng Wang, Tao Kong, Rufeng Zhang +2
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We emplo…
Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation
Yiming Li, Tao Kong, Ruihang Chu +3
Grasping in cluttered scenes has always been a great challenge for robots, due to the requirement of the ability to well understand the scene and object information. Previous works…
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Ruihang Chu, Yukang Chen, Tao Kong +2
Separating 3D point clouds into individual instances is an important task for 3D vision. It is challenging due to the unknown and varying number of instances in a scene. Existing d…
Learning to Design and Construct Bridge without Blueprint
Yunfei Li, Tao Kong, Lei Li +2
Autonomous assembly has been a desired functionality of many intelligent robot systems. We study a new challenging assembly task, designing and constructing a bridge without a blue…
SOLO: A Simple Framework for Instance Segmentation
Xinlong Wang, Rufeng Zhang, Chunhua Shen +2
Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that has made instance segmentation much more challenging. In or…
Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun +4
It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…
Scale-aware Automatic Augmentation for Object Detection
Yukang Chen, Yanwei Li, Tao Kong +4
We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and box-level augmentations are…
Locate then Segment: A Strong Pipeline for Referring Image Segmentation
Ya Jing, Tao Kong, Wei Wang +3
Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature in…