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Found 9 papers, 5 papers with code
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
The past years have witnessed a proliferation of large language models (LLMs).
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
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.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
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.
Graph Sampling-based Meta-Learning for Molecular Property Prediction
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.
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment
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
Comparison of Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
Image Quality Assessment: Unifying Structure and Texture Similarity
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
Intrinsic Image Popularity Assessment
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.
A Simple Method to improve Initialization Robustness for Active Contours driven by Local Region Fitting Energy
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.
