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20242026
most citedMambaIRv2: Attentive State Space Restoration

3 citations · 4 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration

Haoze Sun, Wenbo Li, Jiayue Liu +7

Generalization has long been a central challenge in real-world image restoration. While recent diffusion-based restoration methods, which leverage generative priors from text-to-im…

cs.CV2024

PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution

Libo Zhu, Jianze Li, Haotong Qin +4

Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced…

cs.CV2024

BiDense: Binarization for Dense Prediction

Rui Yin, Haotong Qin, Yulun Zhang +5

Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In th…

cs.CV2024

Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment

Zheng Chen, Xun Zhang, Wenbo Li +7

The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advancement allows for more detailed…

eess.IV2024★ 3 cited

MambaIRv2: Attentive State Space Restoration

Hang Guo, Yong Guo, Yaohua Zha +5

The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causa…