Inception-Atrous-Collation Layers for Interactive Image Segmentation
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Solution Overview
Problem
Traditional atrous convolutional neural networks (CNNs) face issues such as the gridding problem and calibration challenges in the effective receptive field, leading to information loss and degraded performance in high-fidelity vision tasks like pixel-level segmentation.
Innovation Solution
The introduction of inception-atrous-collation (IAC) layers that dynamically combine inception and atrous convolution operations with context-based skip connections, addressing the gridding problem and optimizing the effective receptive field by densely populating sparse signals and reducing feature dilution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If atrous convolution operations are used to increase the receptive field, then the field of view is expanded, but feature density is lost due to the gridding problem and information loss
Solution Approach 1:
The patent segments the convolution operation into two distinct components: atrous convolution for expanding receptive field and dense convolution for preserving feature density. This segmentation allows each component to perform its specialized function without compromising the other, resolving the contradiction between field expansion and information retention.
Solution Approach 2:
The patent introduces an intermediary mechanism (the hybrid convolutional layer) that mediates between atrous convolution and dense convolution operations. This intermediary structure combines the advantages of both approaches, allowing the system to expand receptive field while maintaining feature density through coordinated operation of both convolution types.
2Length of stationary object
If sequential atrous convolutions are applied to expand receptive field, then the field of view increases, but gridding artifacts and calibration challenges arise
Solution Approach 1:
Instead of applying sequential atrous convolutions that create gridding artifacts, the patent inverts the approach by using parallel dense convolutions that operate without the dilation-induced grid structure. This inversion eliminates the gridding problem while achieving comparable receptive field expansion through the hybrid architecture.
Solution Approach 2:
The patent creates a composite convolutional layer that combines atrous and dense convolution operations. This composite structure leverages the receptive field expansion capability of atrous convolution while incorporating the artifact-free feature extraction of dense convolution, achieving both goals simultaneously without the gridding problems of pure atrous approaches.
3Length of stationary object
If deeper CNN layers are used to increase receptive field, then the field of view expands, but feature dilution and performance degradation occur
Solution Approach 1:
The patent ensures continuity of useful action by maintaining dense feature extraction throughout the network architecture. The hybrid convolutional layers continuously preserve feature density across all depths, preventing the feature dilution that occurs in traditional deep CNNs with sequential atrous convolutions, thereby maintaining high segmentation performance at increased depths.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for interactive image segmentation. An example apparatus includes an inception controller to execute an inception sublayer of a convolutional neural network (CNN) including two or more inception-atrous-collation (IAC) layers, the inception sublayer including two or more convolutions including respective kernels of varying sizes to generate multi-scale inception features, the inception sublayer to receive one or more context features indicative of user input; an atrous controller to execute an atrous sublayer of the CNN, the atrous sublayer including two or more atrous convolutions including respective kernels of varying sizes to generate multi-scale atrous features; and a collation controller to execute a collation sublayer of the CNN to collate the multi-scale inception features, the multi-scale atrous features, and eidetic memory features.