Few-Shot Object Detection via kNN Feature Aggregation
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Solution Overview
Problem
Current few-shot object detection (FSOD) methodologies do not fully exploit the provided few-shot image space, analyzing novel training samples one batch at a time without considering the entire data context, leading to suboptimal object detection accuracy.
Innovation Solution
The proposed solution involves optimizing two-stage FSOD frameworks by incorporating an aggregated representation of the entire input space during inference and training. This is achieved through a k-nearest neighbor (kNN) feature weighting technique that calculates a weighted aggregated feature representation for similar images, which is then used to improve the object detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FSOD methodologies analyze novel training samples one batch at a time, then the training process is simpler and faster, but the object detection accuracy is suboptimal because the entire data context is not considered
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing feature representations for all training images before the actual detection phase. This allows the system to leverage the entire data context without increasing training complexity during inference, as the heavy computational work is done in advance during the preliminary feature extraction stage
Solution Approach 2:
The patent introduces an intermediary mechanism - a database storing pre-computed feature representations and a k-nearest neighbor search system. This intermediary layer enables the system to efficiently access and aggregate information from the entire training dataset during inference, improving accuracy without requiring complex training procedures
2Measurement precision
If the entire input space is fully exploited during inference, then object detection accuracy improves, but the computational time and resources increase
Solution Approach 1:
The patent performs feature extraction and representation computation as a preliminary action before inference. By pre-processing the entire training dataset to extract features and store them in a database, the system avoids the time cost of processing the entire input space during inference, while still benefiting from comprehensive data utilization
Solution Approach 2:
The patent extracts only the essential feature representations from the entire input space and stores them in a compact form in a database. During inference, the system extracts and utilizes only the relevant features needed for detection, avoiding unnecessary computational overhead while maintaining accuracy
Data Source
AI summary
Systems, devices, and methods are described herein for improving object detection frameworks. Proposed regions can be used to identify similar images from a novel image set. Once identified, a weighted average of the feature representations of the similar images and/or a probability distribution of the classification labels for those images can be generated. The weighted average of the feature representations and/or the probability distribution can be used to steer the predicted classification confidence and/or predicted bounding box coordinates of the object detection framework. The disclosed techniques can be easily integrated with the object detect framework to improve the accuracy of its predictions without adding additional trainable parameters so as to refrain from adding complexity to the learning process.