Vision-Language Segmentation Masks Without Two-Stage Detection
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Solution Overview
Problem
Existing vision language models (VLMs) lack efficient segmentation functionality capable of responding to natural language prompts, being limited to two-stage models that first output bounding boxes and then perform segmentation, which is inefficient for real-time applications.
Innovation Solution
A single-stage VLM-facilitated segmentation system that processes media and text inputs using a media backbone, text backbone, cross-modality decoder, and convolutional layers to generate segmentation masks directly, with dynamic parameters and additional classification heads for bounding shapes and object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If two-stage models are used for VLM segmentation, then object detection and segmentation can be performed, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The patent merges object detection and segmentation into a single-stage VLM architecture. The model simultaneously performs both functions by processing media inputs through a media backbone, cross-modality decoder, and segmentation head in one unified operation, eliminating the sequential two-stage process and reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The VLM model performs preliminary object detection and feature extraction in advance during the single processing stage, so that segmentation can be performed immediately without waiting for a separate detection phase. This preliminary action enables faster overall processing while maintaining the reliability of both detection and segmentation functions.
2Reliability
If two-stage models are used for VLM segmentation, then segmentation can be performed with bounding boxes, but system complexity increases
Solution Approach 1:
The patent combines multiple functions (object detection, feature extraction, segmentation) into a single integrated VLM architecture. The media backbone, cross-modality decoder, and segmentation head work together in one unified model, reducing architectural complexity compared to separate two-stage models while maintaining comprehensive segmentation functionality.
Solution Approach 2:
The VLM model is designed as a universal system that performs multiple tasks simultaneously - object detection, feature representation, and segmentation - through a single architecture. This multi-functionality reduces the need for separate specialized models, thereby reducing overall system complexity while maintaining reliable segmentation capability.
3Ease of operation
If traditional CV models are used for object detection, then objects can be identified, but natural language prompt response capability is insufficient
Solution Approach 1:
The patent introduces a cross-modality decoder as an intermediary component that bridges natural language prompts and visual segmentation tasks. This decoder processes text inputs from the language backbone and transforms them into guidance signals for the segmentation head, enabling the system to respond to natural language prompts while maintaining fast segmentation processing speeds through efficient multi-modal integration.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques for efficient instance segmentation with vision language models (VLMs). In an embodiment, the techniques include processing an input into the VLM to generate a segmentation map of a media item. The input includes the media item, which includes a plurality of media item units (e.g., pixels, groups of pixels), and further includes a prompt associated with the media item. The segmentation map includes identification of media item units associated with individual objects of one or more objects in the media item, and the VLM includes a dynamic portion having parameters that are determined in view of the media item.