Multi-Modal Feature Contrast for Robust Few-Shot Classification
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Solution Overview
Problem
Existing few-shot classification methods rely solely on visual encoders, neglecting textual content, use suboptimal distance metrics, and lack domain-specific information, limiting generalization performance.
Innovation Solution
Introduce Large Multi-modal Models (LMMs) to generate textual descriptions from images, merging visual and textual embeddings, and apply multi-modal and cross-modal contrast with modal masks to enhance feature representation and distance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images are processed only by a visual encoder in a single-modality manner, then the processing is simple and fast, but textual content features are left hidden and unused, limiting classification performance
Solution Approach 1:
The patent merges visual and textual modalities by processing images through both a visual encoder and a text encoder (via LMMs-generated descriptions), combining their features into a unified multi-modal feature pool that leverages complementary information from both modalities for improved classification
Solution Approach 2:
The system makes the image processing pipeline multi-functional by enabling it to generate both visual features and textual descriptions, allowing the same input image to contribute to multiple feature spaces (visual embedding space and textual embedding space) for comprehensive representation
2Measurement precision
If current methods rely solely on a distance metric by calculating cosine similarity in the visual embedding space, then the computation is efficient, but the metric is suboptimal for few-shot classification
Solution Approach 1:
The patent extends the distance metric from a single visual embedding dimension to multiple dimensions by computing cosine similarity in both visual embedding space and textual embedding space, and further combines them through modal masks to create a multi-dimensional contrastive metric that improves accuracy
Solution Approach 2:
The distance metric is made composite by combining cosine similarity computations from different modalities (visual and textual) with modality-specific masks, creating a hybrid metric that leverages strengths from both embedding spaces
3Adaptability or versatility
If the CLIP classifier weights use domain-agnostic text prompts, then the model has broad generalization ability, but it lacks domain-specific information needed for target tasks
Solution Approach 1:
The patent introduces LMMs (Large Multi-modal Models) as an intermediary that generates textual descriptions from images, which then serve as input to the text encoder. This intermediary component bridges the gap between visual input and textual feature extraction, enabling domain-specific feature generation without directly modifying the CLIP classifier
Solution Approach 2:
The system performs preliminary action by generating textual descriptions and extracting textual features from support images before the classification decision is made. This pre-processing of textual information allows the model to incorporate domain-specific knowledge into the feature pool in advance, improving adaptation to target tasks
Data Source
AI summary
An LMMs-boosted LMC framework system includes a user interface, a multi-modal feature generation module, a multi-modal feature contrast module, and a prediction logic module. The user interface operates in support image mode to process support set images for training, transforming them into text and visual features within a multi-modal support feature pool. In test image mode, the user interface transmits test images to the contrast module, which compares their features with the support pool using visual and textual modalities. The prediction logic module integrates these comparisons to generate a classification index with the most likely classification and confidence score.