There is pre-training, a small 1.3 G, which can be used for auxiliary annotation.
UniRef++ is an innovative technology that can find and accurately mark specific objects in pictures or videos through text descriptions or reference images. Whether it is a still picture or a dynamic video, as long as a description is given or a reference image is provided, UniRef++ can complete the task quickly and accurately. This technology provides tremendous convenience for automatic image editing and video content analysis.
Main features:
Multi-task unified processing: UniRef++ can handle multiple object segmentation tasks simultaneously, such as image segmentation, few-sample image segmentation and object segmentation in videos. Its versatility makes it suitable for a variety of image and video analysis scenarios.
Flexible reference processing: Whether it is text descriptions or annotated masks, UniRef++ can use multiple references to guide segmentation tasks and improve segmentation accuracy and efficiency.
Real-time processing capabilities: Especially in video object segmentation, it can track and segment objects in videos in real time, which is especially important for the analysis of dynamic scenes.
Efficient performance: In multiple benchmark tests, UniRef++ has demonstrated excellent performance, matching or even surpassing the current state-of-the-art technology. Author: AI_Fox https://www.bilibili.com/read/cv28883614/?jump_opus=1 Source: bilibili
Technical principle:
UniFusion module: This is the core of UniRef++, responsible for integrating different types of reference information into the image processing process, so that the model can more accurately understand and locate target objects.
Transformer-based architecture: UniRef++ adopts the Transformer model, a powerful deep learning architecture that can achieve accurate object recognition and segmentation when processing image and video data.
Multi-directional fusion strategy: Flexibly handle different types of input and reference information according to different task requirements.
Instance-level segmentation: It not only identifies objects in images, but also accurately segments each individual instance.
GitHub: https://github.com/FoundationVision/UniRef
Paper: https://arxiv.org/abs/2312.15715 Author: AI_Fox https://www.bilibili.com/read/cv28883614/?jump_opus=1
