dongjunhwang's Stars
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Stability-AI/generative-models
Generative Models by Stability AI
haotian-liu/LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
VikParuchuri/surya
OCR, layout analysis, reading order, table recognition in 90+ languages
salesforce/LAVIS
LAVIS - A One-stop Library for Language-Vision Intelligence
IDEA-Research/GroundingDINO
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
ChaoningZhang/MobileSAM
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
UX-Decoder/Segment-Everything-Everywhere-All-At-Once
[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
SysCV/sam-hq
Segment Anything in High Quality [NeurIPS 2023]
fundamentalvision/Deformable-DETR
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
mahyarnajibi/SNIPER
SNIPER / AutoFocus is an efficient multi-scale object detection training / inference algorithm
IDEA-Research/DINO
[ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"
autodistill/autodistill
Images to inference with no labeling (use foundation models to train supervised models).
luca-medeiros/lang-segment-anything
SAM with text prompt
sfzhang15/RefineDet
Single-Shot Refinement Neural Network for Object Detection, CVPR, 2018
CharlesShang/DCNv2
Deformable Convolutional Networks v2 with Pytorch
IDEA-Research/MaskDINO
[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"
SysCV/sam-pt
SAM-PT: Extending SAM to zero-shot video segmentation with point-based tracking.
lucasjinreal/DCNv2_latest
DCNv2 supports decent pytorch such as torch 1.5+ (now 1.8+)
SHI-Labs/Matting-Anything
Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance.
chongzhou96/MaskCLIP
Official PyTorch implementation of "Extract Free Dense Labels from CLIP" (ECCV 22 Oral)
facebookresearch/stable_signature
Official implementation of the paper "The Stable Signature Rooting Watermarks in Latent Diffusion Models"
Megvii-BaseDetection/DynamicRouting
Learning Dynamic Routing for Semantic Segmentation
hustvl/WeakTr
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
kevin-ssy/CLIP_as_RNN
Official Implementation for CVPR 2024 paper: CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
WalBouss/GEM
[CVPR24] Official Implementation of GEM (Grounding Everything Module)
YimingCuiCuiCui/awesome-instance-segmentation
naver-ai/dual-teacher
Official code for the NeurIPS 2023 paper "Switching Temporary Teachers for Semi-Supervised Semantic Segmentation"
k0u-id/CARB
Official PyTorch implementation of "Weakly Supervised Semantic Segmentation for Driving Scenes", AAAI2024
KOFRJO/values