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20172026
most citedDeep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

90 citations · 277 across the 20 of their papers we have counts for

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13 papers · 1 filter

cs.CV2022

Sim2Real Object-Centric Keypoint Detection and Description

Chengliang Zhong, Chao Yang, Jinshan Qi +4

Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of…

cs.CV2022★ 6 cited

Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation

Sinan Tan, Mengmeng Ge, Di Guo +2

In the Vision-and-Language Navigation task, the embodied agent follows linguistic instructions and navigates to a specific goal. It is important in many practical scenarios and has…

cs.CV2021★ 1 cited

A novel multimodal fusion network based on a joint coding model for lane line segmentation

Zhenhong Zou, Xinyu Zhang, Huaping Liu +3

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architectur…

cs.CV2020★ 21 cited

Unsupervised Representation Learning by InvariancePropagation

Feng Wang, Huaping Liu, Di Guo +1

Unsupervised learning methods based on contrastive learning have drawn increasing attention and achieved promising results. Most of them aim to learn representations invariant to i…

cs.CV2020

Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos

Jianqin Yin, Yanchun Wu, Huaping Liu +3

Action repetition counting is to estimate the occurrence times of the repetitive motion in one action, which is a relatively new, important but challenging measurement problem. To…

cs.CV2019

TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction

Xiaoli Liu, Jianqin Yin, Jin Liu +3

Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new 2D CNN based network, TrajectoryNet, to predict future…