#video segmentation

Segment Anything Model 2 (SAM 2) is designed for efficient image and video segmentation using a transformer architecture. By processing images as single-frame videos, SAM 2 offers extensive segmentation capabilities supported by the largest video dataset. The recent SAM 2.1 update includes new checkpoints and training tools, enhancing performance and usability.

MeViS is a large-scale benchmark focused on improving video segmentation through motion expressions, specifically for complex environments. Concentrating on motion descriptors, it supports the advancement of language-guided technologies and enhances the ability to identify targets using diverse expressions where static imagery is insufficient.

ClipsAI, an open-source Python library, efficiently converts long, audio-focused videos into short clips suitable for podcasts, interviews, and speeches. It uses advanced transcription to identify key segments for clips and dynamically resizes video to focus on speakers, easily changing footage from 16:9 to 9:16 ratios. Discover detailed documentation and view live demonstrations.

UniRef++ introduces a game-changing approach to object segmentation with its unique UniFusion module, enabling seamless integration of reference information through advanced flash attention. This comprehensive model spans across spatial and temporal contexts, supporting tasks like referring image segmentation, few-shot segmentation, and video object segmentation. Acting as a plug-in to enhance foundational frameworks such as SAM, UniRef++ is supplied with complete guides and resources—training instructions, evaluation guidelines, and model checkpoints—to ease your implementation and experimentation process. Unlock new possibilities in segmentation tasks with UniRef++ across various fields.

Explore a pioneering open-source project focusing on video object segmentation and tracking. Featuring Segment Anything Models (SAM) and DeAOT algorithms, it provides both automatic and interactive capabilities, applicable to fields such as smart cities and autonomous driving. Regular updates include features like audio-grounding and a refined WebUI, allowing for efficient object tracking and interaction with video data. Try the Colab demo and utilize a straightforward WebUI for effective object tracking.