Search Results for author: Hanwen Liu

Found 11 papers, 4 papers with code

Multigrid Backprojection Super-Resolution and Deep Filter Visualization

1 code implementation25 Sep 2018 Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu

It is also residual since we use the network to update the outputs of a classic upscaler.

Super-Resolution

Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution

1 code implementation27 Sep 2018 Pablo Navarrete Michelini, Dan Zhu, Hanwen Liu

We describe our solution for the PIRM Super-Resolution Challenge 2018 where we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for RMSE<=12. 5, and 7th best for RMSE<=11. 5.

Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing

MGBPv2: Scaling Up Multi-Grid Back-Projection Networks

1 code implementation27 Sep 2019 Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu

Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and achieved the 5th place in terms of high-fidelity (PSNR).

Image and Video Processing

Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling

no code implementations22 May 2017 Pablo Navarrete Michelini, Hanwen Liu

We interpret convolutional networks as adaptive filters and combine them with so-called MuxOut layers to efficiently upscale low resolution images.

Multi-Grid Back-Projection Networks

no code implementations1 Jan 2021 Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu, Xingqun Jiang

For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output.

Back-Projection Pipeline

no code implementations25 Jan 2021 Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang

We propose a simple extension of residual networks that works simultaneously in multiple resolutions.

Rain Removal Super-Resolution

TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering

no code implementations23 Oct 2023 Fangyu Lei, Tongxu Luo, Pengqi Yang, Weihao Liu, Hanwen Liu, Jiahe Lei, Yiming Huang, Yifan Wei, Shizhu He, Jun Zhao, Kang Liu

Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.

Question Answering

XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision

no code implementations31 Oct 2023 Daniel Hajialigol, Hanwen Liu, Xuan Wang

However, these methods ignore the importance of incorporating the explanations of the generated pseudo-labels, or saliency of individual words, as additional guidance during the text classification training process.

Question Answering Saliency Prediction +2

AI for Biomedicine in the Era of Large Language Models

no code implementations23 Mar 2024 Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu, Hanwen Liu, Meng Lu, Xuan Wang

The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and further extending to societal predictions like infectious disease outbreaks.

Language Modelling Large Language Model

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