Forward Backward Embedding Fusion for Video Panoptic Segmentation
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Solution Overview
Problem
Existing video panoptic segmentation approaches primarily consider only the forward pass of a video, which limits their overall performance in terms of segmentation and tracking accuracy.
Innovation Solution
The proposed solution involves a system and method that utilize both forward and backward embedding fusion (FBEF) to improve the performance of video panoptic segmentation. This is achieved by refining query embeddings in both forward and backward time orders to generate forward and backward embeddings, which are then fused to produce improved classification predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only forward pass of video is considered, then processing simplicity is maintained, but segmentation and tracking accuracy are limited
Solution Approach 1:
The patent applies the inversion principle by processing the video sequence in both forward and backward temporal directions. The embedding module generates forward embeddings by processing frames in chronological order and backward embeddings by processing frames in reverse order. This dual-directional approach allows the system to capture temporal dependencies from both future and past contexts, significantly improving segmentation accuracy and object tracking performance while maintaining manageable processing complexity through structured computation graphs.
2Measurement precision
If only forward pass of video is considered, then computational efficiency is maintained, but tracking accuracy deteriorates
Solution Approach 1:
The system processes video frames in both forward and backward temporal orders to generate complementary embedding representations. The forward embeddings capture temporal evolution from past to future, while backward embeddings capture temporal relationships from future to past. This dual-pass approach enriches the feature representations with bidirectional temporal context, improving tracking accuracy without requiring excessive computational resources by reusing the same embedding module architecture for both directions.
Solution Approach 2:
The patent merges forward embeddings and backward embeddings through a fusion mechanism that combines both temporal direction representations. The fusion module integrates these bidirectional embeddings to create enhanced representation that captures comprehensive temporal information, improving tracking accuracy while maintaining computational efficiency by combining results from both passes rather than processing all possible temporal combinations separately.
3Measurement precision
If forward and backward embedding fusion is applied, then segmentation and tracking accuracy are improved, but processing time increases
Solution Approach 1:
The system implements bidirectional temporal processing by generating forward embeddings in chronological order and backward embeddings in reverse order. Both passes use the same embedding module architecture, allowing for efficient computation. The processing time increase is managed by processing frames in parallel where possible and by reusing the same computational graph structure for both forward and backward passes, thereby minimizing the time penalty of the additional processing direction.
Solution Approach 2:
The fusion module efficiently combines forward and backward embeddings through optimized integration mechanisms. By merging the results from both temporal directions in a structured manner, the system achieves improved segmentation accuracy while controlling processing time. The fusion operation leverages the complementary nature of forward and backward embeddings to produce enhanced representations without requiring redundant computations.
Data Source
AI summary
A system and a method are disclosed for performing video segmentation, including obtaining a plurality of frames from an input video; extracting a plurality of features from the plurality of frames; obtaining query embeddings corresponding to the plurality of features; refining the query embeddings in a forward time order to generate forward embeddings, and in a backward time order to generate backward embeddings; fusing the forward embeddings and the backward embeddings to obtain fused embeddings; and generating a classification prediction corresponding to the input video based on the fused embeddings.