The official implementation of 3DDFA_V3 in CVPR2024 (Highlight).

[Neurips2023] ODE-based Recurrent Model-free Reinforcement Learning for POMDPs

[ICLR 2024 (Spotlight)] "Frozen Transformers in Language Models are Effective Visual Encoder Layers"

A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis

Papers of Medical Image Analysis on CVPR

Official implementation of MICCAI2024 paper "Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis"

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

ICLR 2024: Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gan…

Transparent medical image AI via an image–text foundation model grounded in medical literature

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper "Learning to Receive Help: Intervention-Aware Concept Embed…

The official implementation of the paper **Learning Concise and Descriptive Attributes for Visual Recognition**

👋 Code for : "CRAFT: Concept Recursive Activation FacTorization for Explainability" (CVPR 2023)

[MedIA Best Paper Award] Official implementation of MedIA paper "BayeSeg: Bayesian Modelling for Medical Image Segmentation with Interpretable Generalizability"

An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept data

Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023

Staggeringly powerful macOS desktop automation with Lua

Cornerstone is a set of JavaScript libraries that can be used to build web-based medical imaging applications. It provides a framework to build radiology applications such as the OHIF Viewer.

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

Segment Anything in Medical Images

Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts

UniverSeg: Universal Medical Image Segmentation

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

Low rank adaptation for segmentation anything model (SAM)

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.