Semi-supervised Landmark Localization via Attribute Copying
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Solution Overview
Problem
Training neural networks for landmark detection in images requires large datasets with precise landmark locations, which is time-consuming and difficult to obtain, limiting the effectiveness of existing methods.
Innovation Solution
A semi-supervised learning approach using a small training dataset with precise landmark locations, combined with attribute class labels for semi-supervised training, where a neural network model generates pixel-level likelihood estimates and employs a soft-argmax function to compute predicted coordinates, and a sequential multi-tasking system for improved landmark localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supervised learning techniques are used for landmark detection, then reliable detection can be achieved, but large training datasets with precise landmark locations are required which are time-consuming and difficult to obtain
Solution Approach 1:
The patent uses attribute class labels as proxies or copies of the difficult-to-obtain precise landmark locations. Instead of requiring manually annotated landmark coordinates for every training image, the system uses automatically obtainable attribute labels (e.g., emotion, pose, expression classes) that correlate with landmark positions, thereby copying the essential supervisory signal in a more accessible form
Solution Approach 2:
The patent introduces attribute class labels as an intermediary between the input images and the landmark detection task. These attribute labels serve as a mediating supervisory signal that is easier to obtain than direct landmark annotations, bridging the gap between available data and the desired detection capability
2Measurement precision
If large training datasets with precise landmark locations are obtained, then accurate landmark detection can be achieved, but the data labeling process is tedious and requires high precision manual work
Solution Approach 1:
The system replaces the need for precise manual landmark annotation by using attribute class labels that can be obtained through simpler, more automated processes. These labels copy the essential supervisory information needed for training without requiring tedious pixel-level landmark placement
Solution Approach 2:
The system enables the training data to essentially annotate itself through attribute classification. Rather than requiring external expert annotation of landmark positions, the training process uses attribute labels that can be derived from the images themselves or through simpler classification tasks, making the data preparation process self-service oriented
3Quantity of substance
If semi-supervised learning is used with attribute class labels, then the need for extensive labeled datasets is reduced, but a sequential multi-tasking system is required which increases system complexity
Solution Approach 1:
The patent merges the landmark detection task with attribute classification tasks into a unified sequential multi-tasking system. By combining these tasks and sharing the underlying neural network infrastructure, the system reduces the quantity of labeled data needed while managing complexity through task integration rather than separate independent systems
4Measurement precision
If manual landmark labeling is performed with high precision requirements, then accurate training data can be obtained, but the labeling process becomes extremely time-consuming
Solution Approach 1:
The system uses attribute class labels as a substitute or copy of precise landmark annotations. These attribute labels provide sufficient supervisory signal for training without requiring the high-productivity compromise of reduced precision, effectively decoupling the precision requirement from the productivity constraint
Data Source
AI summary
A method, computer readable medium, and system are disclosed for sequential multi-tasking to generate coordinates of landmarks within images. The landmark locations may be identified on an image of a human face and used for emotion recognition, face identity verification, eye gaze tracking, pose estimation, etc. A neural network model processes input image data to generate pixel-level likelihood estimates for landmarks in the input image data and a soft-argmax function computes predicted coordinates of each landmark based on the pixel-level likelihood estimates.