Few-Shot Object Detection via Dynamic Semantic Network
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Solution Overview
Problem
Few-shot object detection methods are sensitive to data scarcity, particularly in novel classes with limited annotated objects, leading to performance degradation when explicit and implicit shots are varied, due to reliance on visual information alone.
Innovation Solution
The introduction of semantic relation reasoning, where a Semantic Relation Reasoning Few-Shot Detector (SRR-FSD) learns novel objects from both visual and semantic information, using a dynamic relation graph driven by image data to align visual representations with class embeddings, reducing the domain gap between vision and language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If few-shot object detection methods rely solely on visual information from limited annotated samples, then the detector can be trained with minimal data, but performance degrades drastically as data becomes scarce and the detector becomes sensitive to variations in shot numbers
Solution Approach 1:
The patent introduces semantic information as an intermediary between visual data and detection outcomes. A semantic relation module processes language descriptions and knowledge graph data to generate semantic features that mediate the detection process, allowing the system to compensate for insufficient visual samples through enriched semantic representations
Solution Approach 2:
The patent combines multiple information sources (visual features, semantic features, and relation features) into a composite detection framework. This composite approach integrates heterogeneous data types with different characteristics, creating a more robust detection system that maintains performance stability across varying data availability conditions
2Loss of information
If the backbone network is pre-trained on large-scale classification datasets containing implicit shots of novel classes, then the detector gains early access to novel class knowledge, but removing these implicit shots has a negative impact on performance
Solution Approach 1:
The patent segments the feature extraction process into distinct visual and semantic components. By separating these functions, the system can independently optimize each pathway and reduce dependence on the specific composition of pre-trained data, as the semantic module compensates for limitations in either pathway
Solution Approach 2:
The semantic relation module serves multiple functions: it provides class definitions, establishes relationships between classes, and generates semantic features for detection. This multi-functionality reduces the system's dependence on pre-trained data containing implicit shots, as the same semantic module works effectively regardless of the pre-training data composition
3Device complexity
If novel classes are learned exclusively through images with independent learning between classes, then the detection framework remains simple, but visual information becomes limited as image data becomes scarce
Solution Approach 1:
The patent merges independent class learning with relational reasoning by integrating a knowledge graph module that captures inter-class relationships. This combination allows the system to leverage relationships between classes to compensate for limited visual information, as semantic relations provide additional constraints and guidance for learning novel classes
Solution Approach 2:
The patent transitions from purely visual feature space to a multi-dimensional space that includes semantic dimensions. By adding semantic and relational dimensions to the learning framework, the system gains additional information channels that compensate for limited visual data without significantly increasing overall system complexity
Data Source
AI summary
Disclosed herein is an improved few-shot detector which utilizes a dynamic semantic network which takes as input a language feature and generates trainable parameters for a visual network. The visual network takes a visual feature as input and generates a classification and localization of an object.