Image Embedding System Using Adaptive Visual-Semantic Gating
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Solution Overview
Problem
Current image embedding methods for few-shot learning rely solely on visual features, which can lead to information loss when forcing visual and semantic spaces to align, especially when labeled samples are available, as they may ignore the structural differences between these modalities.
Innovation Solution
A method that generates vector representations of images by combining visual and semantic information using a convex combination, where the mixing coefficient is conditioned on semantic label embeddings, ensuring both modalities are on the same dimensional space for effective classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visual and semantic spaces are forced to align using zero-shot learning approaches, then the model can handle unseen classes, but information loss occurs when labeled samples are available
Solution Approach 1:
The patent implements dynamic modality selection by introducing a gating mechanism that adaptively determines whether to use visual features, semantic features, or a combination thereof based on the availability of labeled data. When labeled samples are available, the system dynamically switches to rely more on visual features, avoiding the information loss associated with forcing alignment between visual and semantic spaces. This dynamic adaptation resolves the contradiction by making the alignment process conditional rather than mandatory.
Solution Approach 2:
The patent applies different processing strategies to different modalities based on local conditions. Specifically, it treats visual and semantic modalities differently: visual features are processed through convolutional neural networks when labeled data is available, while semantic features use pre-trained embeddings. The gating mechanism locally determines the optimal processing path for each modality, allowing the system to leverage the strengths of each modality without forcing a harmful global alignment between spaces with different structures.
2Adaptability or versatility
If explicit visual-semantic alignment is enforced, then zero-shot learning becomes feasible, but classification performance deteriorates when labeled samples are available
Solution Approach 1:
The system dynamically adjusts its behavior based on data availability. During training with labeled samples, the model learns to prioritize visual features and does not enforce semantic alignment, achieving high classification accuracy. During test time for unseen classes, the gating mechanism automatically switches to utilize semantic features for alignment-free classification. This dynamic switching resolves the contradiction by making explicit alignment optional rather than mandatory.
Solution Approach 2:
The patent introduces a gating mechanism as an intermediary component that mediates between visual and semantic modalities. This gate learns to select the appropriate modality or combination based on the input characteristics and data availability, avoiding the need for explicit alignment while maintaining zero-shot capability. The intermediary enables the system to leverage semantic information without forcing a harmful alignment between the two spaces.
3Adaptability or versatility
If joint embeddings are learned for semantic and visual features, then both modalities can be utilized, but the structural differences between modalities are ignored
Solution Approach 1:
The patent segments the feature processing into separate pathways for visual and semantic modalities, each maintaining its own structure and characteristics. Visual features are extracted through CNNs while semantic features use pre-trained embeddings, preserving the unique properties of each modality. The gating mechanism then selectively combines these segmented features based on the task requirements, allowing multi-modal utilization without forcing structural alignment that would compromise the integrity of individual modalities.
Data Source
AI summary
There is described a computer-implemented method and system for classifying images, the computer-implemented method comprising: receiving an image to be classified, generating a vector representation of the image to be classified using an image embedding method, comparing the vector representation of the image to predefined vector representations of the predefined image categories, and identifying a relevant category amongst the predefined image categories based on the comparison, the relevant category being associated with the image to be classified and outputting the relevant category.