jingyunliang / pfnet-fgvc

PFNet: A Novel Part Fusion Network for Fine-grained Visual Categorization

Jingyun Liang

Email / Homepage / Google Scholar / Github

I am currently a PhD Student at Computer Vision Lab, ETH Zürich, Switzerland. I am co-supervised by Prof. Luc Van Gool and Prof. Radu Timofte. I also work closely with Dr. Kai Zhang. I mainly focus on low-level vision research, especially on image and video restoration, such as

• image/video super-resolution (SR)
• image/video deblurring
• image/video denoising
• ...

🚀 News

• 2022-10-04: Our new paper RVRT, NeurlPS2022 achieves SOTA video restoration results with balanced size, memory and runtime.
• 2022-08-30: See our papers on real-world image denoising (SCUNet) and video denoising (ReViD).
• 2022-07-30: Three papers, including EFNet (event-based image deblurring, oral), DATSR (reference image SR) and DAVSR (video SR), accepted by ECCV2022.
• 2022-01-28: Our new paper VRT outperforms previous Video SR/ deblurring/ denoising/ frame interpolation/ space-time video SR methods by up to 😍 2.16dB. 😍
• 2021-10-20: SwinIR is awarded the best paper prize in ICCV-AIM2021.
• 2021-08-01: Three papers (HCFlow, MANet and BSRGAN) accepted by ICCV2021.
• 2021-03-29: One paper (FKP) accepted by CVPR2021.

🌱 Repositories

Topic	Title	Badge
real-world video denoising	Practical Real Video Denoising with Realistic Degradation Model
event-based image deblurring	Event-based Fusion for Motion Deblurring with Cross-modal Attention, ECCV2022
reference image SR	Reference-based Image Super-Resolution with Deformable Attention Transformer, ECCV2022
interpretable video restoration	Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV2022
transformer-based video restoration	Recurrent Video Restoration Transformer with Guided Deformable Attention
transformer-based video restoration	VRT: A Video Restoration Transformer
transformer-based image restoration	SwinIR: Image Restoration Using Swin Transformer
real-world image denoising	Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
real-world image SR	Designing a Practical Degradation Model for Deep Blind Image Super-Resolution, ICCV2021
blind image SR	Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution, ICCV2021
blind image SR	Flow-based Kernel Prior with Application to Blind Super-Resolution, CVPR2021
normalizing flow-based image SR and image rescaling	Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling, ICCV2021
image/ video restoration	Image/ Video Restoration Toolbox

pfnet-fgvc's Issues

object proposal

您好， 我对您的工作身份感兴趣，我在ssw生成cub200数据那里有一些问题，希望您可以提供一下cub200生成数据集的代码。

How to train own model?

Thank you for your nice work, and you said 'the training work is like follows',which I don not know.Can you tell me how to train? Thank you very much.

Can I find the paper?

Thanks for your nice work! I am curious that whether you have published the paper because I want to get more detail destricription. Thanks again!

SPP_layer

Thank you again.I'm not sure if my fault. But I didn't see where your spp_layer was.

Files not found

Thank you so much for the implementation of the paper. Was trying to run the code, but the files are not found, like proposed rois, train test split, Getting the following error.
"404. That’s an error.
The requested URL was not found on this server. That’s all we know."
Could you please update the link???

Thanks and Regards
Britty