Lin GU   谷林

I am now a research scientist at RIKEN AIP, Japan (理化学研究所), as well as a special researcher at the University of Tokyo (東京大学).  

I am focused on developing a new generation of artificial intelligence through an evolutionary approach. Existing machine learning methods are designed to mimic human data and are therefore constrained by the limitations of the human brain. To transcend these limitations, I am currently working on directly acquiring recognition abilities and motor skills similar to those developed by humans through evolution.

Following this evolutionary approach, I have developed techniques across various domains, including computer vision, medical imaging, large language models (LLMs), robotics, and even nuclear fusion. I have authored over 60 papers in journals and conferences such as Nature Methods, PAMI, IJCV, AAAI, CVPR, ECCV, ICCV, ICLR, and MICCAI. Additionally, I serve as an associate editor for Pattern Recognition and as an area chair for ICCV, ICML, NeurIPS, and ICLR.

News:

We have two papers accepted at CVPR 2025.

I am honoured to serve as an Area Chair for ICCV 2025

I am honoured to serve as an Area Chair for ICML 2025

We have three papers accepted at AAAI 2025.

Cybernetic Avatar, an open access book about cutting-edge technologies for the development of Cybernetic Avatars I have contributed has been published.

Our paper TinyLUT: Tiny Look-Up Table for Efficient Image Restoration at the Edge has been accepted by Neurips 2024.

Our paper Learning from Human Attention for Attribute-assisted Visual Recognition has been accepted by PAMI.

Our paper Rethinking masked image modeling for medical image representation is published at Medical Image Analysis.

Our paper Frequency-aware Feature Fusion for Dense Image Prediction has been accepted by PAMI.

Our nuclear fusion paper Using Convolutional Neural Networks to detect Edge Localized Modes in DIII-D from Doppler Backscattering measurements has been accepted by Review of Scientific Instruments

Our paper Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning has been accepted by Medical Image Analysis.

Our paper A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning  has been accepted by IEEE Transactions on Medical Imaging 

We have three papers accepted at ECCV 2024.

I am honoured to serve as an Area Chair for Neurips 2024

I've joined the Pattern Recognition Journal as an Associate Editor. 

Our journal paper, BigNeuron: a resource to benchmark and predict the performance of algorithms for automated tracing of neurons in light microscopy datasets, is published by Nature Methods.

RIKEN has issued a news release about our ECCV 2022 paper: Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection.