Meta has presented ai.meta.com/blog/segment-anything-2/”>SAM2the next generation of its Segment Anything model. Building on the success of its predecessor, SAM 2 is an innovative unified model designed for real-time object segmentation in images and videos. SAM 2 extends the capabilities of the original, image-focused SAM. The new model integrates seamlessly with video data, delivering real-time object segmentation and tracking across all frames. This capability is achieved without the need for custom tuning, thanks to SAM 2’s ability to generalize to new and unseen visual domains. The model’s zero-shot generalization means it can segment any object in any video or image, making it highly versatile and adaptable to a variety of use cases.
One of the most notable features of SAM 2 is its efficiency. It requires less interaction time – three times less than previous models – while achieving superior accuracy in image and video segmentation. This efficiency is crucial for practical applications where time and accuracy are essential.
The potential applications of SAM 2 are wide and varied. For example, in the creative industry, the model can generate new video effects, enhancing the capabilities of generative video models and opening up new avenues for content creation. In data annotation, SAM 2 can speed up the labeling of visual data, thereby improving the training of future machine vision systems. This is especially beneficial for industries that rely on large data sets for training, such as autonomous vehicles and robotics.
The SAM 2 is a promising model in the scientific and medical fields, as it can segment moving cells in microscopic videos, facilitating research and diagnostic processes. The model's ability to track objects in drone images can help monitor wildlife and conduct environmental studies.
In keeping with Meta’s commitment to open science, the SAM 2 project includes the publication of the model code and weights under an Apache 2.0 license. This openness fosters collaboration and innovation within the ai community, allowing researchers and developers to explore new capabilities and applications of the model. Meta has published the SA-V dataset, a comprehensive collection of approximately 51,000 real-world videos and over 600,000 spatiotemporal masks, under a CC BY 4.0 license. This dataset is significantly larger than previous datasets, providing a valuable resource for training and testing segmentation models.
The development of SAM 2 involved significant technical innovations. The model architecture builds on the foundations established by SAM, extending its capabilities to handle video data. This involves a memory mechanism that allows the model to retrieve previously processed information and accurately segment objects in video frames. The memory encoder, memory bank, and memory attention module are critical components that enable SAM 2 to handle the complexities of video segmentation such as object motion, deformation, and occlusion.
The SAM 2 team developed a programmable visual segmentation task to address the challenges posed by video data. This task allows the model to take input cues at any video frame and predict a segmentation mask, which is then propagated across all frames to create a spatiotemporal mask. This iterative process ensures accurate and refined segmentation results.
In conclusion, SAM 2 offers unparalleled real-time object segmentation capabilities in images and videos. Its versatility, efficiency, and open-source nature make it a valuable tool for many applications, from creative industries to scientific research. By sharing SAM 2 with the global ai community, Meta fosters innovation and collaboration, paving the way for future advancements in machine vision technology.
"Up until today, annotating masklets in videos has been clunky; combining the first SAM model with other video object segmentation models. With SAM 2 annotating masklets will reach a whole new level. I consider the reported 8x speedup to be the lower bound of what is achievable with the right UX, and with +1M inferences with SAM on the Encord platform, we’ve seen the tremendous value that these types of models can provide to ML teams. " - Dr Frederik Hvilshøj - Head of ML at Encord
Review the ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/” target=”_blank” rel=”noreferrer noopener”>Paper, Download the template, ai.meta.com/datasets/segment-anything-video/” target=”_blank” rel=”noreferrer noopener”>Data setand Try the demo hereAll credit for this research goes to the researchers of this project. Also, don't forget to follow us on twitter.com/Marktechpost”>twitter and join our Telegram Channel and LinkedIn GrAbove!. If you like our work, you will love our Newsletter..
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