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Sun Yat-sen University
- Guangzhou, Guangdong
- http://marshal-r.iteye.com
Stars
[CVPR 2025] Official code repository for "MaSS13K: A Matting-level Semantic Segmentation Benchmark"
OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting
[NeurIPS'24] A Simple Image Segmentation Framework via In-Context Examples
DINO-X: The World's Top-Performing Vision Model for Open-World Object Detection and Understanding
TPAMI:Frequency-aware Feature Fusion for Dense Image Prediction
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
A curated list of foundation models for vision and language tasks
Official repository for "AM-RADIO: Reduce All Domains Into One"
High-resolution models for human tasks.
微信公众号:机器感知 | Tracking the Latest Layer Diffusion Trending
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
A procedural Blender pipeline for photorealistic training image generation
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
Awesome OVD-OVS - A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
Production-ready platform for agentic workflow development.
Famous Vision Language Models and Their Architectures
Segment Matting is a project aimed at improving the quality and performance of image matting using the SAM (Segment Anything Model) model. It focuses on optimizing the matting process to reduce jag…
EasyPortrait - Face Parsing and Portrait Segmentation Dataset
A minimal GPU design in Verilog to learn how GPUs work from the ground up
This is the repo for our new project Highly Accurate Dichotomous Image Segmentation
Rembg is a tool to remove images background
Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts (CVPR 2024)
Code for the paper "Benchmarking Object Detectors with COCO: A New Path Forward."