Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework

arXiv:2609.02529 · cs.CV, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

Image Quality Assessment — 13× in this paper
Benchmark — 8× in this paper · explained in 2 episodes
Transformer — 8× in this paper · explained in 2 episodes
Verification — 7× in this paper · explained in 2 episodes
Ablation — 6× in this paper · explained in 1 episode
Locality-Aware Dynamic Task Prioritization — 4× in this paper
Distillation — 3× in this paper · explained in 2 episodes
Generalization — 3× in this paper · explained in 13 episodes
Ablation study — 2× in this paper · explained in 1 episode
Fidelity — 2× in this paper · explained in 2 episodes
In European Conference on Computer Vision — 2× in this paper
Representation Learning — 2× in this paper · explained in 2 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Causal Inference — 1× in this paper · explained in 4 episodes
Continual Learning — 1× in this paper · explained in 2 episodes
Contrastive Learning — 1× in this paper · explained in 5 episodes
Core — 1× in this paper · explained in 2 episodes
Dynamic Weighting — 1× in this paper · explained in 3 episodes
Hallucination — 1× in this paper · explained in 3 episodes
Inspired by Dynamic Task Prioritization — 1× in this paper
Instruction Tuning — 1× in this paper · explained in 2 episodes
Knowledge Distillation — 1× in this paper · explained in 11 episodes
Reconstruction Error — 1× in this paper · explained in 2 episodes

Sources

A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Related papers