Numerous applications, such as robotics, autonomous driving, and video editing, benefit from video segmentation. Deep neural networks have come a long way in recent years. However, existing approaches need help with untested data, especially zero-shot scenarios. These models need specific video segmentation data to tune them to maintain consistent performance in various scenarios. In a zero-shot setup, or when these models are transferred to untrained video domains and span object categories that fall outside the training distribution, current methods in Video Object Segmentation (VOS) Semi-Supervised and Video Instance Segmentation (VIS) shows performance gaps when it comes to unseen data.
The use of successful models from the image segmentation domain for video segmentation tasks offers a potential solution to these problems. The Segment Anything (SAM) concept is one such promising concept. With a staggering 11 million images and over a billion masks, the SA-1B dataset served as the training ground for SAM, a solid foundation model for image segmentation. SAM’s extraordinary zero-shot generalization abilities are made possible by his massive training set. The model has been shown to perform reliably in various downstream tasks using zero-shot transfer protocols, is highly customizable, and can create high-quality skins from a single foreground point.
SAM exhibits strong zero-shot image segmentation abilities. However, it is naturally not suitable for video segmentation problems. SAM has recently been modified to include video segmentation. As an illustration, TAM combines SAM with the next generation memory based mask tracker XMem. Similar to how SAM-Track combines DeAOT with SAM. While these techniques greatly restore SAM performance on distribution data, they fall short when applied to more difficult zero shot conditions. Many segmentation problems can be solved by visual cues using other techniques that don’t need SAM, including SegGPT, although they still require a mask annotation for the initial video frame.
This issue poses a substantial obstacle to zero-trigger video segmentation, especially as researchers work to create simple techniques to generalize to new situations and reliably produce high-quality segmentation across multiple video domains. Researchers from ETH Zurich, HKUST and EPFL present SAM-PT (Segment Anything Meets Point Tracking). This approach offers a new approach to the problem by being the first to segment videos using sparse point tracking and SAM. Instead of using mask propagation or object-centric dense feature matching, they suggest a point-based method that uses the detailed local structural data encoded in movies to track points.
Because of this, you only need to score scattered points in the first frame to indicate the target item, and it offers superior generalization to invisible objects, a strength that was proven in the open-world UVO benchmark. This strategy effectively expands SAM’s capabilities for video segmentation while preserving its inherent flexibility. Using the adaptability of modern point trackers such as PIPS, SAM-PT requests from SAM scattered point trackers predicted with these tools. They concluded that the most appropriate approach to motivate SAM was to initialize locations to track using K-Medoids cluster centers from a mask tag.
It is possible to clearly distinguish between background and target elements by tracking positive and negative points. They suggest different mask decoding processes that use both points to further improve the output masks. They also developed a point reset technique that improves tracking accuracy over time. In this method, points that have been unreliable or obscured are discarded, and points from sections or segments of the object that become visible on successive frames are added, such as when the object rotates.
In particular, their test results show that SAMPT performs equal to or better than existing zero-trigger approaches in various video segmentation benchmarks. This shows how adaptable and reliable your method is because no video segmentation data was required during training. In zero-trigger configurations, SAM-PT can speed up progress on video segmentation tasks. Their website has multiple interactive video demos.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
