Question: In SAM 2, which is developed by Meta, what is the position of memory attention relative to the mask decoder, before or after?
Date: 2024-08-01

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Introducing SAM 2: The next generation of Meta Segment Anything Model ...
Following up on the success of the Meta Segment Anything Model (SAM) for images, we're releasing SAM 2, a unified model for real-time promptable object segmentation in images and videos that achieves state-of-the-art performance.; In keeping with our approach to open science, we're sharing the code and model weights with a permissive Apache 2.0 license.

Meta Segment Anything Model 2
SAM 2 also supports the ability to make corrections in the mask prediction based on additional prompts on any frame. SAM 2's streaming architecture—which processes video frames one at a time—is also a natural generalization of SAM to the video domain. When SAM 2 is applied to images, the memory module is empty and the model behaves like SAM.

SAM 2: Segment Anything in Images and Videos - GitHub
Segment Anything Model 2 (SAM 2) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing.

SAM 2: Segment Anything in Images and Videos | Research - AI at Meta
We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks.

Meta SAM 2: Architecture, Applications & Limitations - Analytics Vidhya
Meta's SAM-2 advances computer vision with real-time image and video segmentation, building on its predecessor's capabilities. SAM-2 enhances Meta AI's models, extending from static image segmentation to dynamic video tasks with new features and improved performance. SAM-2 supports video segmentation, unifies architecture for image and ...

From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model
The architecture of SAM 2 introduces several improvements, involving a layered masked encoder, Hiera, and memory attention mechanism. These, in turn, facilitate the processing of high-resolution features and the integration of temporal data, improving the segmentation process in videos. ... Introducing sam 2: The next generation of meta segment ...

Segment Anything - Meta Research
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions ...

facebookresearch/segment-anything - GitHub
Meta AI Research, FAIR. Alexander Kirillov, Eric Mintun, ... The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, ...

Answer: before