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20202023
most citedCLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

318 citations · 361 across the 6 of their papers we have counts for

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eess.IV2023★ 318 cited

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

Jie Liu, Yixiao Zhang, Jie-Neng Chen +7

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem…

eess.IV2021★ 7 cited

AdaDM: Enabling Normalization for Image Super-Resolution

Jie Liu, Jie Tang, Gangshan Wu

Normalization like Batch Normalization (BN) is a milestone technique to normalize the distributions of intermediate layers in deep learning, enabling faster training and better gen…

eess.IV2021★ 1 cited

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Andrey Ignatov, Radu Timofte, Maurizio Denna +20

Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task…

eess.IV2021

Anchor-based Plain Net for Mobile Image Super-Resolution

Zongcai Du, Jie Liu, Jie Tang +1

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing…

eess.IV2020★ 32 cited

Residual Feature Distillation Network for Lightweight Image Super-Resolution

Jie Liu, Jie Tang, Gangshan Wu

Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-b…