AI ethics evidence index Search
Source-linked daily record
Evidence record 953 · automatically gathered

Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment

Existing vision-language model (VLM)-based AI-generated image quality assessment (AIGIQA) methods suffer from a fundamental semantic-distortion dimensional conflict: monolithic representations optimized for semantic discrimination inherently entangle compositional understanding with low-level perceptual sensitivity, rendering them blind to fine-grained quality degradations. We introduce MST-CLIPIQA, a multi-scale two-stream framework that achieves hierarchical vision-language alignment through e

Record details

Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline

ParaScale: Scale-Calibrated Camera-Motion Transfer via a Gauge-Invariant Parallax Number

arXiv · 18 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment

arXiv · 9 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation

arXiv · 8 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Affective AI Safety: The Missing Piece in LLM Safety

arXiv · 22 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval

arXiv · 23 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

arXiv · 3 June 2026

How to cite this record

ethics.ai (15 June 2026), “Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment,” evidence record 953, https://ethics.ai/record/953 (originally published by arXiv).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.