tianyu0207 Goto Github PK

acpl

Code for "ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification" [CVPR 2022]

al-folio

A beautiful, simple, clean, and responsive Jekyll theme for academics

alrploss

Official PyTorch Implementation of aLRP Loss [NeurIPS2020]

awesome-anomaly-detection

A curated list of awesome anomaly detection resources

awesome-contrastive-self-supervised-learning

A comprehensive list of awesome contrastive self-supervised learning papers.

awesome-semi-supervised-learning

😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

awesome-temporal-action-detection-temporal-action-proposal-generation

Temporal Action Detection & Weakly Supervised Temporal Action Detection & Temporal Action Proposal Generation

awesome-visual-transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

ccd

Code for 'Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images' [MICCAI 2021]

fishyscapes

Benchmark for Anomaly Detection in Semantic Segmentation

fsad-net

Offical code for 'Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy' [MICCAI 2020]

igd

Official code for 'Deep One-Class Classification via Interpolated Gaussian Descriptor' [AAAI 2022 Oral]

memmc-mae

Official code for 'Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder'

pebal

[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

pmsacl

Code for 'Self-supervised Multi-class Pre-training forUnsupervised Anomaly Detection and Segmentation in Medical Images'

rtfm

Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

samed

The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"

semi-chest

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

weakly-polyp

[MICCAI'22] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.