X-Prompt - A general framework for multimodal video object segmentation

What is X-Prompt?

X-Prompt is a general framework for multimodal video object segmentation, addressing the limitations of traditional methods in complex scenarios such as extreme lighting, fast motion, and background interference. It adapts a pre-trained base model for video object segmentation based on RGB data to downstream multimodal tasks by using additional modal information (such as thermal imaging, depth, or event camera data) as visual cues.

Main functions of X-Prompt

• Multimodal adaptationX-Prompt uses a multimodal visual cue (MVP) to encode additional modal information into visual cues, and combines this with RGB data to enhance the segmentation capabilities of the base model in multimodal tasks.
• Maintain generalization abilityBy using multimodal adaptive experts (MAEs), X-Prompt provides specific knowledge for each modality without compromising the generalization ability of the base model, thus avoiding model collapse that may result from full parameter fine-tuning.
• Efficient task migrationIt can quickly adapt to new downstream tasks with limited multimodal labeled data, reducing the research workload and hardware costs of designing and training models for each task separately.
• Multi-task integrationX-Prompt supports a variety of multimodal tasks (such as RGB-T, RGB-D, and RGB-E), and integrates tasks through a unified framework, which significantly improves the performance of models in complex scenarios.

The technical principles of X-Prompt

• Base model pre-trainingX-Prompt's base model is a video object segmentation model based on Vision Transformer. It is pre-trained using a large number of RGB video sequences to achieve powerful segmentation and generalization capabilities. The goal of the pre-training phase is to enable the model to segment target objects in the current frame based on reference frames and their segmentation masks.
• Multimodal Visual Cue (MVP)The role of MVP is to encode additional modal information (such as thermal imaging, depth, or event camera data) into visual cues and integrate them into the base model. Through multi-scale convolutional embedding layers, MVP embeds RGB and X modal image patches into multi-scale cues, which are used to guide the base model for object segmentation.
• Multimodal Adaptation Expert (MAE)MAE provides specific knowledge for each modality through a low-rank adaptation mechanism, while retaining the general capabilities of the base model.MAE is designed to allow for fine-tuning of models to adapt to specific multimodal tasks without compromising the generalization ability of the base model.

X-Prompt's project address

• arXiv technical paper:https://arxiv.org/pdf/2409.19342

Application scenarios of X-Prompt

• autonomous drivingBy combining thermal imaging and depth information, X-Prompt can more accurately identify and segment targets on the road, improving the safety of autonomous driving systems.
• Robot VisionIn complex environments, multimodal information can help robots more accurately identify and manipulate target objects.
• Video surveillanceIn surveillance scenarios with low light or complex backgrounds, X-Prompt can improve the accuracy of target detection and segmentation using multimodal data.