Stars
Collect some World Models for Autonomous Driving papers.
[Image and Vision Computing (Vol.147 Jul. '24)] Interactive Natural Image Matting with Segment Anything Models
[Information Fusion (Vol.103, Mar. '24)] Boosting Image Matting with Pretrained Plain Vision Transformers
Official repository for the paper F, B, Alpha Matting
Unofficial Visual Prompt Tuning implementation
codebase for The Role of Data Curation in Image Captioning
(CVPR 2024) ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
[CVPR 2023] Explicit Visual Prompting for Low-Level Structure Segmentations
❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119
Python implementation of A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2006, New York
YChuan1115 / Level-Set-1
Forked from Ramesh-X/Level-SetLevel Set algorithm for python
A python implementation of exposure fusion
[CVPR 2024] Official implement of <Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation>
[IPCAI'2024 (IJCARS special issue)] Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Official implementation for AnomalyCLIP (ICLR 2024)
Segment Anything combined with CLIP
Code for the model to segment people at the image
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"
Official repo for "Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models"