most citedA Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation

2 citations · 6 across the 10 of their papers we have counts for

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cs.CV2026

MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling

Rong Fu, Chunlei Meng, Yangchen Zeng +9

Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming…

cs.CV2026

SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery

Rong Fu, Jiekai Wu, Xiaowen Ma +4

Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster response, yet remains difficult due to il…

cs.CV2026

DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles

Rong Fu, Jiekai Wu, Yang Li +3

The emergence of 3D Gaussian Splatting has fundamentally redefined the capabilities of photorealistic neural rendering by enabling high-throughput synthesis of complex environments…

cs.CV2025

VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm

Zhenkai Wu, Xiaowen Ma, Zhenliang Ni +4

Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devi…

cs.CV2025

Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion

Yizhen Jiang, Mengting Ma, Anqi Zhu +3

Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images, finall…

cs.CV2025

Cott-ADNet: Lightweight Real-Time Cotton Boll and Flower Detection Under Field Conditions

Rui-Feng Wang, Mingrui Xu, Matthew C Bauer +3

Cotton is one of the most important natural fiber crops worldwide, yet harvesting remains limited by labor-intensive manual picking, low efficiency, and yield losses from missing t…