Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
May 20, 2024 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
A collection of resources on applications of Transformers in Medical Imaging.
Recent Transformer-based CV and related works.
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
[NeurIPS 2021] [T-PAMI] DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
EsViT: Efficient self-supervised Vision Transformers
Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".
[NIVT Workshop @ ICCV 2023] SeMask: Semantically Masked Transformers for Semantic Segmentation
A Monocular depth-estimation for in-the-wild AutoFocus application.
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (ICCV 2023)
Official PyTorch implementation of Fully Attentional Networks
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.
Implementation of MetNet-3, SOTA neural weather model out of Google Deepmind, in Pytorch
[ICLR'24 Spotlight] Uni3D: 3D Visual Representation from BAAI
Official repository for "Self-Supervised Video Transformer" (CVPR'22)
Determine whether a given video sequence has been manipulated or synthetically generated
Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
An unofficial implementation of ViTPose [Y. Xu et al., 2022]
Library - Vanilla, ViT, DeiT, BERT, GPT
[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
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