Video Instance Segmentation Transformer With Multi-Scale Split Attention
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Solution Overview
Problem
Existing transformer-based video instance segmentation approaches fail to capture multi-scale spatio-temporal feature relationships, leading to challenges in accurately segmenting and tracking objects due to scale variation, aspect-ratio change, and fast motion in videos, particularly in automotive computer vision systems.
Innovation Solution
A video instance segmentation transformer architecture with a multi-scale spatio-temporal split (MS-STS) attention module and temporal attention block, combined with an adversarial loss, to enhance foreground-background separability and temporal consistency, enabling accurate segmentation and tracking of objects despite deformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing transformer-based video instance segmentation approaches are used, then the system can process video data, but it fails to capture multi-scale spatio-temporal feature relationships, leading to inaccurate segmentation and tracking under scale variation, aspect-ratio change, and fast motion
Solution Approach 1:
The attention mechanism is segmented into spatio-temporal split attention modules that separately process spatial and temporal features at multiple scales. This segmentation allows the system to capture fine-grained spatio-temporal relationships that improve segmentation accuracy under various transformations.
Solution Approach 2:
The system extends feature processing from single-scale to multi-scale by incorporating features at different spatial resolutions and temporal depths. This dimensional expansion enables the model to capture objects at various sizes and motion speeds, improving adaptability to scale variation and aspect-ratio changes.
2Device complexity
If single-scale attention mechanisms are used in transformer-based segmentation, then the model structure remains simple, but it cannot accurately track objects undergoing fast motion, scale variation, or aspect-ratio change
Solution Approach 1:
The multi-scale feature pyramid structure nests features at different resolutions within a hierarchical framework. Coarse-scale features provide global context while fine-scale features capture detailed object boundaries, enabling reliable tracking under deformation without excessive complexity.
Solution Approach 2:
The system performs preliminary spatio-temporal feature extraction and alignment before the main segmentation process. By pre-processing features to account for motion and scale variations, the model maintains tracking accuracy even when objects undergo fast motion or aspect-ratio changes.
3Ease of manufacture
If standard transformer architecture is used for video instance segmentation, then implementation is straightforward, but temporal consistency of segmentation masks deteriorates across video frames
Solution Approach 1:
The temporal attention mechanism continuously aggregates information across video frames, maintaining a persistent representation of object instances. This continuous temporal processing ensures that segmentation masks remain consistent across frames while preserving the straightforward transformer implementation structure.
4Measurement precision
If multi-scale spatio-temporal split attention modules are added to capture feature relationships, then segmentation accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The deformable attention mechanism selectively attends to only the most relevant spatio-temporal locations rather than processing all positions uniformly. This partial action approach captures essential multi-scale relationships for accurate segmentation while reducing unnecessary computations to maintain processing speed.
Data Source
AI summary
A system, method, computer readable storage medium for a computer vision system includes at least one video camera, and video processor circuitry. The method includes inputting a stream of video data and generating a sequence of image frames, segmenting and tracking, by the video analysis apparatus, object instances in the stream of video data, including receiving the sequence of image frames, analyzing the sequence of image frames using a video instance segmentation transformer to obtain a video instance mask sequence from the sequence of image frames, the transformer having a backbone network, a transformer encoder-decoder, and an instance matching and segmentation block, The encoder contains a multi-scale spatio-temporal split attention module to capture spatio-temporal feature relationships at multiple scales across multiple frames. The decoder contains a temporal attention block for enhancing a temporal consistency of transformer queries. The method includes displaying the video instance mask sequence.