Testing adaptation of the DINOv2 encoder for vision tasks with Low-Rank Adaptation (LoRA)

The first LVLM based IAD method!

Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [ICML2023]

CVPR 2023 Workshop VAND Challenge: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning

Paper list and datasets for industrial image anomaly detection (defect detection). 工业异常检测(瑕疵检测)论文及数据集检索库。

Unofficial implementation of EfficientAD https://arxiv.org/abs/2303.14535

📈 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.

Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文总结+润色+审稿+审稿回复

(TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey

CVPR 2023 论文和开源项目合集

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

MemAE for anomaly detection. -- Gong, Dong, et al. "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection". ICCV 2019.

Official PyTorch implementation for "Mixed supervision for surface-defect detection: from weakly to fully supervised learning"

An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.

Implementation for the paper (CVPR Oral): High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks