AI Godmother Fei-Fei Li Has a Vision for Computer Vision
Stanford University professor Fei-Fei Li has already earned her place in the history of AI. She played a major role in the deep learning revolution by laboring for years to create the ImageNet dataset and competition, which challenged AI systems to recognize objects and animals across 1,000 categories. In 2012, a neural network called AlexNet sent shockwaves through the AI research community when
Update: Expanding access to Meta Segment Anything 2.1 on Amazon SageMaker JumpStart
Update: Expanding access to Meta Segment Anything 2.1 on Amazon SageMaker JumpStart Updated February 12, 2025: Last July, we released Meta Segment Anything 2, a follow-up to our popular open source segmentation model, offering developers a unified model for real-time promptable object segmentation and tracking in images and videos. We’ve been blown away by the impact SAM 2 has made across the comm
Introducing Segment Anything: Working toward the first foundation model for image segmentation
Segment Anything’s promptable design enables flexible integration with other systems. SAM could receive input prompts, such as a user’s gaze from an AR/VR headset, like Project Aria. SAM: A generalized approach to segmentation Previously, to solve any kind of segmentation problem, there were two classes of approaches. The first, interactive segmentation, allowed for segmenting any class of object
GitHub - IDEA-Research/DINO: [ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"
[2023/7/10] We release Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Code and checkpoint are available! [2023/4/28]: We release a strong open-set object detection and segmentation model OpenSeeD that achieves the best results on open-set object segmentation tasks. Code and checkpoints are available here. [2023/4/26]: DINO is
![GitHub - IDEA-Research/DINO: [ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"](https://cdn-ak-scissors.b.st-hatena.com/image/square/11c75bfdae1c9c97914a55e578bd643d0a29542d/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F94a7a2bca92883e461346ce99f2561246f72bd7de00b0d4623b4420e8ac7d407%2FIDEA-Research%2FDINO)
GitHub - IDEA-Research/Grounded-Segment-Anything: Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
We plan to create a very interesting demo by combining Grounding DINO and Segment Anything which aims to detect and segment anything with text inputs! And we will continue to improve it and create more interesting demos based on this foundation. And we have already released an overall technical report about our project on arXiv, please check Grounded SAM: Assembling Open-World Models for Diverse V
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Clothing dataset (full, high resolution)
iMaterialist (Fashion) 2020 at FGVC7
Papers with Code - MS COCO Dataset
https://dl.acm.org/doi/10.1007/978-3-031-19836-6_31
Meta AI開発者に聞く「オープンなAI」と「認識のためのAI」【西田宗千佳のイマトミライ】
Improving Apparel Detection with Category Grouping and Multi-grained Branches
FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation
GitHub - xushilin1/FashionFormer: Code for our ECCV-2022 work: Fashionformer A simple, effective and unified baseline for human fashion segmentation and recognition
GitHub - Cartucho/mAP: mean Average Precision - This code evaluates the performance of your neural net for object recognition.
Segment Anything
Segment Anything
ピンタレストが日本で急成長 Z世代にユーザー層拡大 - 日本経済新聞
End-to-end object detection with Transformers | Research - AI at Meta
GitHub - google-research/scenic: Scenic: A Jax Library for Computer Vision Research and Beyond
GitHub - IDEA-Research/GroundingDINO: [ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"![GitHub - IDEA-Research/GroundingDINO: [ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"](https://cdn-ak-scissors.b.st-hatena.com/image/square/3574292e2a3b5adb181786a76443d8d8a031e003/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F05a34fe919b844f81b3c5b5ac093e2a6543c35694f175177ee12ba21dc09df12%2FIDEA-Research%2FGroundingDINO)
