Two-Pass CNN for High-Resolution Object Proposals
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Solution Overview
Problem
Current deep-learning models for object identification in images face challenges in accurately detecting objects of varying sizes and positions within images, often requiring extensive computational resources and achieving high recall and localization accuracy.
Innovation Solution
A two-pass convolutional neural network architecture is employed, utilizing a feed-forward network with both forward-pass and backward-pass layers to generate high-level and low-level information, enabling the generation of high-resolution object proposals and scores, and refining these proposals through a bottom-up/top-down architecture for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep-learning models are used to generate object proposals for images, then object identification capability is improved, but computational resources required increase
Solution Approach 1:
The system segments the object identification process into two distinct passes: a forward pass that generates coarse object proposals and a backward pass that refines them into high-resolution masks. This segmentation allows the model to process information at different levels of detail, improving object identification capability while managing computational resources more efficiently by avoiding full high-resolution processing for all regions.
Solution Approach 2:
The two-pass architecture applies partial action by first generating low-resolution object proposals that cover potential object regions, then applying excessive action only where needed by refining those specific regions in the backward pass. This approach achieves high object identification accuracy without applying full computational effort uniformly across the entire image.
2Measurement precision
If high-resolution object proposals are generated, then localization accuracy is improved, but computational complexity increases
Solution Approach 1:
The system divides the computational workload into two passes: the forward pass generates coarse object proposals at lower resolution, and the backward pass refines only the identified object regions to high resolution. This segmentation achieves high localization accuracy for objects while avoiding the computational complexity of processing entire images at full resolution.
Solution Approach 2:
The two-pass architecture introduces a temporal dimension to the processing pipeline, using the forward pass to establish object regions and the backward pass to refine them. This dimensional approach allows the system to achieve high localization accuracy without requiring all computational resources to operate at maximum resolution simultaneously.
3Adaptability or versatility
If objects of varying sizes and positions are detected, then adaptability is improved, but recall and precision maintainability becomes difficult
Solution Approach 1:
The two-pass convolutional neural network architecture dynamically adapts to objects of varying sizes and positions by first identifying potential regions in the forward pass and then refining them in the backward pass. This dynamic two-stage approach maintains high recall and precision across diverse object configurations, as the system can adjust its processing focus based on the specific characteristics of each detected object.
Solution Approach 2:
The backward pass uses feedback from the forward pass results to refine object proposals. The system takes the coarse proposals generated in the first pass and uses them as input to the second pass, which applies refinement operations to improve precision. This feedback mechanism ensures that recall and precision are maintained even when detecting objects of varying sizes and positions throughout the image.
Data Source
AI summary
In one embodiment, a feature map of an image having h×w pixels and a patch having one or more pixels of the image are received. The patch has been processed by a first set of layers of a convolutional neural network and contains an object centered within the patch. The patch is then processed using the feature map and one or more pixel classifiers of a classification layer of a deep-learning model, where the classification layer includes h×w pixel classifiers, with each pixel classifier corresponding to a respective pixel of the patch. Each of the pixel classifiers used to process the patch outputs a respective value indicating whether the corresponding pixel belongs to the object centered in the patch.