AnoCLIP: Text-Guided Zero-shot Anomaly Localization via Self-Supervised Adaptation
Bibliographic record
Abstract
This study aims to explore the intrinsic potential of CLIP (Contrastive Language-Image Pretraining) for zero-shot anomaly localization, a challenging task that involves localizing anomalous regions without normal samples. To this end, we propose AnoCLIP, an efficient and training-free framework for zero-shot anomaly localization. By employing visual attention modifications and comprehensive text prompts, we effectively harness CLIP’s natural capability for zero-shot anomaly localization. To further refine the results of AnoCLIP in visual anomaly localization, we propose a novel test-time adaptation method, Text-Guided Anomaly Self-Supervised Adaptation (TGASA). Our approach constructs a pair of balanced self-supervised tasks using text-prompted pseudo-labels and noise-corrupted visual tokens, respectively, to optimize a lightweight adapter during inference. The proposed approach is time-efficient and significantly enhances the anomaly localization performance of AnoCLIP. Through comprehensive experiments, we demonstrate the efficiency and effectiveness of AnoCLIP and TGASA in zero-shot anomaly localization on various datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".