activity
20152026
most citedRecurrent Neural Networks for Multivariate Time Series with Missing Values

231 citations · 703 across the 56 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 1 cited

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

Chengxiang Yin, Zhengping Che, Kun Wu +2

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are in…

cs.CV2023

Cross-Modal Reasoning with Event Correlation for Video Question Answering

Chengxiang Yin, Zhengping Che, Kun Wu +3

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the…

cs.CV2023★ 6 cited

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Haowen Wang, Zhipeng Fan, Zhen Zhao +7

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric feature…

cs.CV2023★ 13 cited

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

Haowen Wang, Zhengping Che, Yufan Yang +6

Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent…

cs.RO2023★ 1 cited

CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network

Mingze Wei, Yaomin Huang, Zhiyuan Xu +7

In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly red…

cs.CV2023★ 3 cited

CP: Channel Pruning Plug-in for Point-based Networks

Yaomin Huang, Ning Liu, Zhengping Che +7

Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success ha…