Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
Oct 19, 2024 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Recent Transformer-based CV and related works.
A collection of resources on applications of Transformers in Medical Imaging.
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
[NeurIPS 2021] [T-PAMI] DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
[ICLR'24 Spotlight] Uni3D: 3D Visual Representation from BAAI
Official PyTorch implementation of Fully Attentional Networks
EsViT: Efficient self-supervised Vision Transformers
Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.
[NIVT Workshop @ ICCV 2023] SeMask: Semantically Masked Transformers for Semantic Segmentation
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (ICCV 2023)
Implementation of MetNet-3, SOTA neural weather model out of Google Deepmind, in Pytorch
Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
[ECCV 2022] The official repo for the paper "Poseur: Direct Human Pose Regression with Transformers".
Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.
A Monocular depth-estimation for in-the-wild AutoFocus application.
An unofficial implementation of ViTPose [Y. Xu et al., 2022]
Official repository for "Self-Supervised Video Transformer" (CVPR'22)
Few-Shot Diffusion Models
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