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20232026
most citedCM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation

25 citations · 30 across the 9 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.LG2024

Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model

Xiaochong Dong, Jun Dan, Yingyun Sun +3

Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Ov…

cs.CV2024

FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization

Cheng Yu, Haoyu Xie, Lei Shang +4

In the field of human-centric personalized image generation, the adapter-based method obtains the ability to customize and generate portraits by text-to-image training on facial da…

cs.CV2024★ 3 cited

TopoFR: A Closer Look at Topology Alignment on Face Recognition

Jun Dan, Yang Liu, Jiankang Deng +4

The field of face recognition (FR) has undergone significant advancements with the rise of deep learning. Recently, the success of unsupervised learning and graph neural networks h…

cs.CV2024★ 25 cited

CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation

Mushui Liu, Jun Dan, Ziqian Lu +3

Due to the large-scale image size and object variations, current CNN-based and Transformer-based approaches for remote sensing image semantic segmentation are suboptimal for captur…

cs.CV2024

FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio

Chao Xu, Yang Liu, Jiazheng Xing +8

In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and I…

cs.CV2024

PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation

Haoyu Xie, Changqi Wang, Jian Zhao +4

Tremendous breakthroughs have been developed in Semi-Supervised Semantic Segmentation (S4) through contrastive learning. However, due to limited annotations, the guidance on unlabe…