Video Instance Segmentation Using Query Embedding Matching
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Solution Overview
Problem
Performing video instance segmentation in videos with multiple objects requires significant computing resources, memory, and time, and existing methods often necessitate extensive video-based training and manual annotation of all frames.
Innovation Solution
A query-based image instance segmentation model is applied independently to video frames, using a framework that allows for bipartite matching of query embeddings to track objects across frames without requiring video-based training or manual annotation of all frames, reducing computational demands and annotation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video-based training and manual annotation of all frames are used, then segmentation accuracy is improved, but computational resources and time are significantly increased
Solution Approach 1:
The system performs preliminary action by training only on selected key frames rather than all video frames. The query-based model learns from a subset of annotated frames and then applies this knowledge to unannotated frames through temporal matching, eliminating the need for time-consuming annotation of every frame while maintaining segmentation accuracy
Solution Approach 2:
The video processing is segmented into distinct stages: selecting key frames for annotation, training the model on these frames, and then applying the trained model to remaining frames. This segmentation of the video stream into processed and unprocessed portions allows efficient use of annotation resources
2Measurement precision
If video-based training and manual annotation of all frames are used, then segmentation accuracy is improved, but computing resources are significantly increased
Solution Approach 1:
The system applies partial action by annotating and training on only a portion of video frames rather than all frames. The query-based instance segmentation model processes a subset of frames with full annotation detail, then uses temporal matching to extend results to remaining frames, reducing computational resource requirements while maintaining accuracy
Solution Approach 2:
The system creates copies of query embeddings from annotated frames and applies them to unannotated frames through temporal matching. This copying mechanism allows the model to transfer segmentation knowledge across frames without re-processing each frame through the full annotation pipeline, significantly reducing computational resources
3Stability of the object's composition
If traditional video instance segmentation methods are used, then temporal consistency is achieved, but annotation requirements for all frames are increased
Solution Approach 1:
The system performs preliminary action by establishing temporal relationships and query embeddings from annotated frames before processing unannotated frames. This preliminary setup enables the model to maintain temporal consistency across frames without requiring annotation of every frame, as the temporal matching mechanism carries forward object identities and segmentation information
Solution Approach 2:
Query embeddings serve as intermediaries between annotated and unannotated frames. These embeddings capture object characteristics and temporal relationships, allowing the system to maintain consistency across frames while avoiding the need for direct annotation of each frame. The query-based model acts as a mediator that transfers information through the video sequence
Data Source
AI summary
Apparatuses, systems, and techniques to track one or more objects in one or more frames of a video. In at least one embodiment, one or more objects in one or more frames of a video are tracked based on, for example, one or more sets of embeddings.