Video Instance Segmentation With Temporal Feature Calibration
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Solution Overview
Problem
Conventional video instance segmentation methods suffer from pixel-level and instance-level inconsistencies, leading to inaccurate mask predictions and inefficient use of computing resources due to the decoupled generation and association of mask proposals across frames, lacking temporal context and object-level knowledge.
Innovation Solution
A segmentation system that integrates object-level knowledge into dense pixel embeddings using residual connections and memory of past frames to improve mask predictions, leveraging temporal context for consistent and accurate object segmentation across video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional video instance segmentation methods generate and associate mask proposals independently across frames, then device complexity is reduced, but manufacturing precision deteriorates due to pixel-level and instance-level inconsistencies
Solution Approach 1:
The patent merges independent frame processing with temporal context integration by combining current frame features with historical frame information through residual connections. This creates a unified segmentation approach that maintains consistency across frames while avoiding the need for separate detection and tracking systems, thus improving mask prediction accuracy without proportionally increasing device complexity.
Solution Approach 2:
The patent performs preliminary calibration of features using temporal context from past frames before generating mask predictions for the current frame. By pre-calibrating features with historical information through residual connections, the system ensures consistent pixel-level and instance-level predictions across frames, improving manufacturing precision while maintaining a streamlined processing pipeline.
2Manufacturing precision
If temporal context and object-level knowledge are integrated into dense pixel embeddings, then manufacturing precision improves through consistent mask predictions, but device complexity increases due to residual connections and memory requirements
Solution Approach 1:
The patent introduces calibrated features as an intermediary between raw frame features and final mask predictions. These calibrated features serve as a mediator that integrates temporal context and object-level knowledge through residual connections, improving prediction consistency while organizing the complexity into a manageable intermediate representation that can be efficiently processed.
Solution Approach 2:
The patent maintains continuous integration of temporal context across frames through residual connections that preserve historical information. By continuously calibrating features with past frame data and object-level knowledge, the system achieves consistent mask predictions across the video sequence, improving manufacturing precision while distributing the computational load evenly across frames.
3Manufacturing precision
If features are calibrated across frames using residual connections, then manufacturing precision improves through frame-dependent features, but loss of energy increases due to additional computing operations
Solution Approach 1:
The patent extracts and reuses calibrated features and temporal context from previous frames through residual connections, avoiding redundant computation. By taking out and reutilizing previously computed feature representations and temporal information, the system improves accuracy of object-level predictions while reducing the energy cost of recalculating features from scratch for each frame.
4Manufacturing precision
If pixel-level embeddings are generated from calibrated features, then manufacturing precision improves through accurate mask predictions, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent performs preliminary calibration of features using temporal context and object-level knowledge before generating pixel-level embeddings. By pre-calibrating features with historical information through residual connections, the system ensures accurate pixel-level segmentation while organizing processing steps efficiently, reducing the time penalty of additional processing by performing calibration upfront rather than during embedding generation.
Data Source
AI summary
Embodiments are disclosed for performing video instance segmentation to mask objects across frames of a video. The method may include obtaining a frame of a video sequence where the frame depicts an object. The method further includes determining a calibrated feature of the frame using temporal information associated with a past frame. The method further includes determining a pixel embedding using the calibrated feature. The method further includes determining an object token using a past object token associated with the past frame and the pixel embedding. The method further includes generating a masked frame using the object token and the pixel embedding. The masked frame includes a masked object corresponding to the object.