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Found 6 papers, 3 papers with code
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
Ranked #9 on
Anomaly Detection
on VisA
A Meta-Learning Approach to Predicting Performance and Data Requirements
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
Guided Recommendation for Model Fine-Tuning
With thousands of historical training jobs, a recommendation system can be learned to predict the model selection score given the features of the dataset and the model as input.
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
Visual anomaly detection is commonly used in industrial quality inspection.
Ranked #15 on
Anomaly Detection
on VisA
(Detection AUROC metric)
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain.
