5 code implementations • CVPR 2023 • Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer
Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
Ranked #9 on Anomaly Detection on VisA
no code implementations • CVPR 2023 • Achin Jain, Gurumurthy Swaminathan, Paolo Favaro, Hao Yang, Avinash Ravichandran, Hrayr Harutyunyan, Alessandro Achille, Onkar Dabeer, Bernt Schiele, Ashwin Swaminathan, Stefano Soatto
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
no code implementations • CVPR 2023 • Hao Li, Charless Fowlkes, Hao Yang, Onkar Dabeer, Zhuowen Tu, Stefano Soatto
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.
no code implementations • 13 Sep 2022 • Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
1 code implementation • 28 Jul 2022 • Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer
Visual anomaly detection is commonly used in industrial quality inspection.
Ranked #15 on Anomaly Detection on VisA (Detection AUROC metric)
1 code implementation • 22 Jul 2022 • Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer
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.