2 code implementations • 10 Apr 2024 • Kehua Feng, Keyan Ding, Kede Ma, Zhihua Wang, Qiang Zhang, Huajun Chen
The past years have witnessed a proliferation of large language models (LLMs).
no code implementations • 16 Jan 2024 • Yixuan Li, Peilin Chen, Hanwei Zhu, Keyan Ding, Leida Li, Shiqi Wang
The perceptual quality is quantified by the variant Mahalanobis Distance between the inner and outer Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images.
no code implementations • 5 Oct 2023 • Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen
To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.
1 code implementation • 29 Jun 2023 • Xiang Zhuang, Qiang Zhang, Bin Wu, Keyan Ding, Yin Fang, Huajun Chen
To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction.
no code implementations • 16 Oct 2021 • Keyan Ding, Yi Liu, Xueyi Zou, Shiqi Wang, Kede Ma
The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations.
Ranked #34 on Video Quality Assessment on MSU SR-QA Dataset
1 code implementation • 4 May 2020 • Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
2 code implementations • 16 Apr 2020 • Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.
Ranked #31 on Video Quality Assessment on MSU SR-QA Dataset
1 code implementation • 3 Jul 2019 • Keyan Ding, Kede Ma, Shiqi Wang
The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet.
no code implementations • 28 Feb 2018 • Keyan Ding, Linfang Xiao
Active contour models based on local region fitting energy can segment images with intensity inhomogeneity effectively, but their segmentation results are easy to error if the initial contour is inappropriate.