most citedLAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration

Zhe Dong, Yuzhe Sun, Haochen Jiang +2

Remote sensing images inevitably suffer from various degradation factors during acquisition, including atmospheric interference, sensor limitations, and imaging conditions. These c…

cs.CV2025

MFAF: An EVA02-Based Multi-scale Frequency Attention Fusion Method for Cross-View Geo-Localization

YiTong Liu, TianZhu Liu, YanFeng GU

Cross-view geo-localization aims to determine the geographical location of a query image by matching it against a gallery of images. This task is challenging due to the significant…

cs.CV2025

EarthMapper: Visual Autoregressive Models for Controllable Bidirectional Satellite-Map Translation

Zhe Dong, Yuzhe Sun, Tianzhu Liu +2

Satellite imagery and maps, as two fundamental data modalities in remote sensing, offer direct observations of the Earth's surface and human-interpretable geographic abstractions,…

cs.RO2024

SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images

Junqiu Yu, Xinlin Ren, Yongchong Gu +7

Language-guided robotic grasping is a rapidly advancing field where robots are instructed using human language to grasp specific objects. However, existing methods often depend on…

cs.RO20241 cited

LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion

Jinyu Zhang, Yongchong Gu, Jianxiong Gao +5

This paper addresses the challenge of perceiving complete object shapes through visual perception. While prior studies have demonstrated encouraging outcomes in segmenting the visi…