Video Semantic Segmentation Boundary Loss for Temporal Consistency
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Solution Overview
Problem
Deep neural networks struggle with maintaining temporal consistency and accuracy in video semantic segmentation, particularly at boundary regions, due to the dynamics of content from frame to frame, leading to flickering and inconsistent predictions, and existing methods relying on optical flow estimation introduce additional errors and computational overhead.
Innovation Solution
A novel temporal semantic boundary loss (T-SBL) that does not rely on optical flow or motion estimation, using an L1 loss between ground truth and predicted temporal semantic boundaries to improve consistency and accuracy without increasing computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If optical flow estimation is used to improve temporal consistency, then temporal consistency is improved, but measurement precision deteriorates due to occlusion problems and large displacement limitations
Solution Approach 1:
The patent extracts and removes the optical flow estimation component from the temporal consistency loss calculation. Instead of relying on optical flow to identify corresponding pixels between frames, the method directly compares segmentation predictions at the same spatial locations, eliminating the source of estimation errors while maintaining temporal consistency guidance.
Solution Approach 2:
The patent applies asymmetric treatment to different spatial regions by using occlusion masks to selectively apply temporal consistency loss. Regions identified as occluded are excluded from the TC loss calculation, allowing the model to focus on reliable regions while avoiding the precision degradation that would occur if optical flow were used to handle occluded regions.
2Stability of the object's composition
If optical flow estimation and warping operations are used to achieve temporal consistency, then temporal consistency is improved, but device complexity increases due to additional computational requirements
Solution Approach 1:
The patent removes the optical flow estimation network and warping operations from the architecture. The temporal consistency loss is computed by directly comparing segmentation logits at corresponding spatial positions between consecutive frames, eliminating the need for separate optical flow computation and image warping steps, thereby reducing device complexity.
Solution Approach 2:
The patent merges the temporal consistency enforcement directly into the segmentation network's loss function without requiring separate optical flow estimation and warping modules. This integration eliminates redundant computational components and streamlines the overall system architecture.
3Stability of the object's composition
If optical flow estimation is used for temporal consistency loss, then temporal consistency is improved, but productivity decreases due to increased training time and memory consumption
Solution Approach 1:
The patent extracts and eliminates the optical flow estimation step from the training pipeline. By computing temporal consistency loss directly on segmentation predictions without requiring optical flow computation, the training process becomes faster and consumes less memory, directly improving productivity.
Solution Approach 2:
The patent performs preliminary computation of occlusion masks using simple thresholding on prediction differences before computing the temporal consistency loss. This preliminary action avoids the need for computationally intensive optical flow estimation during training, enabling faster iteration and improved training speed.
Data Source
AI summary
Disclosed is a method including receiving, in a semantic segmentation network, input data from a plurality of frames, computing a ground truth label on the plurality of frames, generating a ground truth temporal semantic boundary map from the ground truth label on the plurality of frames, generating a predicted temporal semantic boundary map based on an output of the input data, and determining a loss based on the ground truth temporal semantic boundary map and the predicted temporal semantic boundary map.