Grounded Prompting for Referring Video Object Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for referring video object segmentation (RVOS) face challenges such as computational expense, time-consuming end-to-end training, and the need for dense mask annotations, while foundation segmentation models struggle with natural language descriptions and video data.
Innovation Solution
A method involving grounded prompting and adaptation using a GroPrompt framework that generates temporal-consistent, text-aware position prompts for RVOS by leveraging image-based foundation segmentation models, employing Text-Contrastive Prompt Learning and Modality-Contrastive Prompt Learning to align frame-level and video-level bounding boxes with text prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end training is used for vision-language models in RVOS, then the model can handle video segmentation tasks, but the computational cost and training time become excessively high
Solution Approach 1:
The patent segments the RVOS task into two independent parts: (1) a vision-language model that processes video frames and text prompts to generate bounding boxes, and (2) a foundation segmentation model that processes individual images to generate masks. This segmentation allows each component to be trained separately and efficiently, avoiding the computational burden of end-to-end training while maintaining overall task reliability.
Solution Approach 2:
The patent introduces bounding box predictions as an intermediary between the vision-language model and the foundation segmentation model. The vision-language model outputs bounding boxes that serve as spatial prompts for the foundation segmentation model, which then generates the final segmentation masks. This intermediary approach enables efficient separate training of both models while achieving accurate video object segmentation.
2Reliability
If end-to-end training is used for vision-language models in RVOS, then the model can handle video segmentation tasks, but the training process becomes time-consuming
Solution Approach 1:
The patent divides the training process into two separate phases: training the vision-language model on video frames and text prompts to generate bounding boxes, and training the foundation segmentation model on image data to generate masks. This segmentation of training tasks significantly reduces the time required compared to end-to-end training, as each model can be optimized independently using appropriate datasets and loss functions.
Solution Approach 2:
The patent performs preliminary training of the foundation segmentation model on static image data before using it for video segmentation tasks. This preliminary action allows the segmentation model to learn optimal features and parameters in advance, so that when combined with the vision-language model for video processing, the overall system achieves high accuracy without requiring time-consuming joint training.
3Measurement precision
If dense mask annotations are required for training, then the model can achieve accurate segmentation, but the scalability of the approach is impeded
Solution Approach 1:
The patent extracts the segmentation functionality from the vision-language model and places it in a separate foundation segmentation model that processes individual images. This extraction allows the vision-language model to be trained with relatively simple bounding box annotations rather than requiring dense mask annotations, significantly improving scalability while the foundation segmentation model handles the complex mask generation task.
Solution Approach 2:
The patent uses a foundation segmentation model that can be trained on standard image datasets with conventional annotations, copying the successful training paradigm from image segmentation to video segmentation. This approach allows the system to leverage existing annotated image data and training methodologies, making the system more scalable and adaptable to different video datasets without requiring new annotation protocols.
4Productivity
If foundation segmentation models are used, then training efficiency improves, but they are not tailored to handle natural language descriptions and video data in RVOS
Solution Approach 1:
The patent creates a universal system where the vision-language model can process various types of input (video frames, text prompts) and the foundation segmentation model can process various types of input (images, bounding box prompts). The system combines these two models to achieve multi-functionality: understanding natural language descriptions of objects in video and generating corresponding segmentation masks, thereby adapting foundation models to the specific RVOS task requirements.
Solution Approach 2:
The patent uses bounding box predictions as an intermediary that bridges the vision-language model's understanding of natural language and video content with the foundation segmentation model's image processing capabilities. The vision-language model generates bounding boxes that capture spatial information from video frames and text descriptions, which then serve as effective prompts for the foundation segmentation model to generate accurate masks, thus adapting the system to handle both natural language and video data.
Data Source
AI summary
Referring Video Object Segmentation (RVOS) aims to segment an object referred to by a sentence query throughout an entire video. In contrast to Referring Image Segmentation (RIS), RVOS is particularly faced with dynamic visual challenges, such as position and size variation, pose deformation, object occlusion or exit, and scene variation. Moreover, the referring sentence may contain long-term motions or actions, which may not be easily recognized from a single frame. Existing works that address this challenging task generally require end-to-end training for vision-language models, which can be computationally expensive and time-consuming, while the requirement of dense mask annotations for training impedes the scalability of those approaches. The present disclosure uses grounded prompting to adapt image-based segmentation models to video object segmentation tasks, which can be achieved with relying only on weak supervision.