activity
20242026
most citedDual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

56 citations · 89 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2026

Post-Training Zero-Shot TTS for Fine-Grained Emotion and Duration Control via Natural Language

Lianru Gao, Yujie Guo, Yong Qin

Audiobook narration, conversational agents, and audiovisual dubbing require fine-grained control over emotion and pacing within an utterance, yet many text-to-speech (TTS) systems…

cs.CV2026

EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models

Yuanchao Su, Lianru Gao, Mengying Jiang +3

Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguou…

cs.SD2025

Towards Automatic Evaluation and High-Quality Pseudo-Parallel Dataset Construction for Audio Editing: A Human-in-the-Loop Method

Yuhang Jia, Hui Wang, Xin Nie +3

Audio editing aims to manipulate audio content based on textual descriptions, supporting tasks such as adding, removing, or replacing audio events. Despite recent progress, the lac…

cs.CV2024★ 23 cited

Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification

Zhu Han, Ce Zhang, Lianru Gao +4

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of…

eess.IV2024★ 56 cited

Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

Zhu Han, Jin Yang, Lianru Gao +3

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited b…

cs.CV2024★ 10 cited

SpectralKAN: Weighted Activation Distribution Kolmogorov-Arnold Network for Hyperspectral Image Change Detection

Yanheng Wang, Xiaohan Yu, Yongsheng Gao +6

Kolmogorov-Arnold networks (KANs) represent data features by learning the activation functions and demonstrate superior accuracy with fewer parameters, FLOPs, GPU memory usage (Mem…