Text-Guided Image Token Labeling for Multi-Object Classification
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Solution Overview
Problem
Existing image classification techniques are inadequate for accurately and efficiently classifying images with multiple objects or unseen categories, especially in high-resolution scenarios, leading to limitations in multi-label classification and increased computational costs.
Innovation Solution
The method involves dividing input images into multiple patches and down-sampling them to generate intermediate images, using these patches and embeddings to train a deep learning model with an attention mechanism guided by textual embeddings, and employing dual-modal decoders to align visual and textual features for multi-label classification, including zero-shot scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image classification techniques are used, then processing speed is maintained at acceptable levels, but classification accuracy for high-resolution images with multiple objects deteriorates
Solution Approach 1:
The input image is divided into multiple patches at different resolution levels (first plurality of patches at original resolution, second plurality of patches at down-sampled resolution). This segmentation allows the system to process high-resolution images by handling smaller, manageable portions while maintaining overall accuracy through multi-scale analysis.
Solution Approach 2:
The patent introduces a multi-resolution dimension by creating intermediate images at different downsampling levels. This adds a resolution dimension to the traditional single-resolution input, enabling the model to capture both fine-grained details from high-resolution patches and contextual information from down-sampled representations.
2Measurement precision
If high-resolution images are processed to maintain detail, then object recognition accuracy improves, but computational costs increase
Solution Approach 1:
By segmenting the image into patches and processing them at different resolution levels, the system reduces the computational burden on any single processing unit while maintaining overall recognition accuracy through multi-scale feature extraction.
Solution Approach 2:
Different patches are processed at different quality levels (resolutions). Critical regions or patches requiring fine detail are processed at higher resolution, while less critical regions use lower resolution processing, optimizing the balance between accuracy and computational cost.
3Adaptability or versatility
If single-label classification is used, then model complexity is reduced, but adaptability to multi-object images deteriorates
Solution Approach 1:
The patent employs a unified transformer encoder-decoder architecture that processes multiple patches at different resolutions through a single model framework. This multi-functional system can handle single-label and multi-label classification tasks, as well as zero-shot learning, without requiring separate specialized models for each function.
Solution Approach 2:
The patent introduces intermediate representations (intermediate images and feature tokens) as mediators between the input patches and the final classification output. These intermediaries organize the complex multi-scale patch information into a structured representation that the classification head can process effectively, managing model complexity through hierarchical abstraction.
Data Source
AI summary
The present invention is directed to image classification techniques. In a specific embodiment, the present invention provides an image processing method. An input image is divided into a first plurality of patches and down-sampled to generate a first intermediate image. The first plurality of patches and the first intermediate image are used to generate a plurality of image tokens, which is used to train a deep learning model for image classification. A textual embedding extracted from a text input is used to guide the plurality of image tokens via an attention mechanism during the training process. There are other embodiments as well.