Deep Learning Clothing Attribute Classification via Mask Tensor Segmentation
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Solution Overview
Problem
Existing clothing attribute recognition models struggle to accurately classify outer-layer and inner-layer clothing when worn in layers, leading to inaccurate predictions and increased labeling burdens, as they lack sufficient information to differentiate between the two layers in training data.
Innovation Solution
A deep-learning-based model that includes a first classification unit, a mask generation unit, and a second classification unit, which generate and apply mask tensors to segment the body and extract features, allowing for accurate classification of clothing attributes without additional labeling, using a weakly supervised learning method to supplement insufficient information labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing clothing attribute recognition models are used to classify layered clothing, then the model can process images efficiently, but the classification accuracy deteriorates because the model cannot distinguish between outer-layer and inner-layer clothing
Solution Approach 1:
The patent segments the clothing recognition task into two distinct classification processes: one for outer-layer clothing and one for inner-layer clothing. The model separately processes and classifies each layer type, preventing the confusion that occurs when treating layered clothing as a single entity. This segmentation approach directly addresses the information loss by maintaining distinct classification pathways for each clothing layer.
Solution Approach 2:
The patent introduces a new dimension to the classification problem by adding layer type differentiation. Instead of classifying clothing attributes in a single dimension, the model now operates in an expanded dimensional space that includes outer-layer and inner-layer distinctions. This dimensional expansion allows the model to preserve and utilize layer information that would otherwise be lost in traditional single-pass classification.
2Measurement precision
If additional labeling data for outer-layer and inner-layer clothing is created to improve classification accuracy, then the model can distinguish between layers, but the data preparation burden and time increase significantly
Solution Approach 1:
The patent performs preliminary segmentation of clothing items into outer-layer and inner-layer categories during the data preparation phase, but uses automated methods rather than manual labeling for each layer. By establishing the classification framework and automated processing pipelines in advance, the system reduces the need for time-consuming manual labeling while maintaining high classification accuracy.
Solution Approach 2:
The model employs self-supervised learning mechanisms where the system automatically generates training signals from the data itself without requiring extensive manual annotation. The clothing recognition model learns to distinguish between outer and inner layers by leveraging the structural relationships and visual patterns inherent in the images, reducing dependency on labor-intensive labeling processes.
3Device complexity
If the model classifies only single garment types to maintain simplicity, then the model structure remains simple, but the model fails to accurately represent real-world scenarios where people wear multiple layers
Solution Approach 1:
The patent segments the clothing classification task into distinct outer-layer and inner-layer classification streams. Each stream processes specific layer types independently, allowing the model to handle multiple garments while maintaining relatively simple individual classification modules. This segmentation enables the model to represent complex real-world layered scenarios without requiring a single overly complex classification structure.
Solution Approach 2:
The patent creates a multi-functional classification system where the model can handle both single-garment and multi-layer scenarios using the same framework. The classification model is designed to be universal, processing different clothing layer combinations through consistent outer-layer and inner-layer classification pathways, thereby maintaining structural simplicity while improving real-world applicability.
Data Source
AI summary
Disclosed herein are an apparatus and method for classifying clothing attributes based on deep learning. The apparatus includes memory for storing at least one program and a processor for executing the program, wherein the program includes a first classification unit for outputting a first classification result for one or more attributes of clothing worn by a person included in an input image, a mask generation unit for outputting a mask tensor in which multiple mask layers respectively corresponding to principal part regions obtained by segmenting a body of the person included in the input image are stacked, a second classification unit for outputting a second classification result for the one or more attributes of the clothing by applying the mask tensor, and a final classification unit for determining and outputting a final classification result for the input image based on the first classification result and the second classification result.