Recent advancements in computer vision have led to the development of Open-Vocabulary SAM, a model that integrates the Segment Anything Model (SAM)—which produces object masks based on input prompts—with the CLIP recognition model. This enhanced model is capable of segmenting and recognizing an impressive range of approximately 22,000 classes, surpassing the capabilities of standard SAM-CLIP combinations. The Open-Vocabulary SAM model, presented by MMLabNTU, utilizes two unique knowledge transfer modules, SAM2CLIP and CLIP2SAM, which enable it to outperform the naive combination of SAM and CLIP. The model's segmentation capabilities are extended with CLIP-like real-world recognition, while also significantly reducing computational costs. The research highlights the importance of Multimodal Embeddings and has sparked interest for its potential applications in various fields such as autonomous vehicles, robotics, and more. The code for this model is built on mmdetection, using the ImageNet-22k and V3Det dataset, and is part of the OpenMMLab project.
[CV] Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively https://t.co/bD9sa5qPgG This paper presents a unified framework that integrates the Segment Anything Model (SAM) and the CLIP model, introducing the Open-Vocabulary SAM model for… https://t.co/dwxcEAHNX2
Open-Vocabulary SAM. This research combines SAM segmentation with CLIP recognition using 2 unique modules SAM2CLIP and CLIP2SAM and significantly outperforms naive combining of CLIP and SAM. https://t.co/emi5CtlQBR
Open-Vocabulary SAM, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM. The former adapts SAM's knowledge into the CLIP via distillation and learnable transformer… https://t.co/tDiRlRlM6f
Thank AK, for sharing. Our work explores a better combination of SAM and CLIP without introducing huge costs. It is a new type of SAM that can segment and recognize over 22k classes. (using ImageNet-22k and V3Det dataset). Our code is built on mmdetection. @OpenMMLab https://t.co/aU6MA6Eoxd
📢Hot new research alert: Open-Vocabulary SAM from @MMLabNTU. This research combines SAM segmentation with CLIP recognition using 2 unique modules SAM2CLIP and CLIP2SAM and significantly outperforms naive combining of CLIP and SAM. Scroll for more details! https://t.co/It5V70SCMO
CLIP Model and The Importance of Multimodal Embeddings https://t.co/iDty2v2pdF #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles #NeuroMorphic #Robotics
mmlab-ntu presents Open-Vocabulary SAM Segment and Recognize Twenty-thousand Classes Interactively paper page: https://t.co/peIaOIQZg3 Open-Vocabulary SAM extends SAM's segmentation capabilities with CLIP-like real-world recognition, while significantly reducing computational… https://t.co/K3LPpIx9YF
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively Can segment and recognize approximately 22k classes proj: https://t.co/6DwBg0Pc4C repo: https://t.co/XbWdQMtsKv abs: https://t.co/Dk2xJRFnhO https://t.co/zIOF1Pwzbb
Open-Vocabulary SAM merges SAM’s segmentation with CLIP’s recognition, using knowledge transfer for enhanced performance in both areas. it effectively handles 22,000 classes, surpassing standard SAM-CLIP combinations. ↓ https://t.co/x86fU7Emc8
Explore YOLO-NAS and SAM for video segmentation. 📹 But first, what is SAM? 🖼️ The Segment Anything Model (SAM) produces object masks based on input prompts like points or boxes. It can be used to create masks for every object present in an image, making it adaptable to a… https://t.co/VZlYqJ3zcR