Seoyoon Jang / 장서윤's repositories
Uniformaly
Uniformaly: Towards Task-Agnostic Unified Anomaly Detection
DL_paper_review
issues에 논문 요약
ALIGN
Repo of NeurIPS23
AnomalyGPT
[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
ARPL
[TPAMI 2021] Adversarial Reciprocal Points Learning for Open Set Recognition
AudioMAE
This repo hosts the code and models of "Masked Autoencoders that Listen".
Awesome-Few-Shot-Class-Incremental-Learning
Awesome Few-Shot Class-Incremental Learning
Awesome-Incremental-Learning
Awesome Incremental Learning
awesome-industrial-anomaly-detection
Paper list and datasets for industrial image anomaly/defect detection (updating).
Awesome-Novel-Class-Discovery
A list of papers that studies Novel Class Discovery
Awesome_Prompting_Papers_in_Computer_Vision
A curated list of prompt-based paper in computer vision and vision-language learning.
baekjoon
코딩테스트 대비 문제집(Baekjoon Online Judge)
DenseCLIP
[CVPR 2022] DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
docker_hub
Process of pushing and pulling images to Docker Hub
EfficientAD
unofficial version of EfficientAD
MOOD
Official PyTorch implementation and pretrained models of Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling Is All You Need (MOOD in short). Our paper is accepted by CVPR2023.
multimodal-prompt-learning
[CVPR 2023] Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".
OOP_study
Object-oriented Programming Study
productive-box
Are you an early 🐤 or a night 🦉? Let's check out in gist
RD4AD
Anomaly Detection via Reverse Distillation from One-Class Embedding
robustlearn
Robust machine learning for responsible AI
RPL
[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Segment-Any-Anomaly
Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".
UniAD_species
Extension code for Multi-class Anomaly Detection with species dataset
VAND-APRIL-GAN
CVPR 2023 Workshop VAND Challenge: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
WinClip
[CVPR 2023] Unofficial re-implementation of "WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation".