Adaptive Object Detection via Latent Representation Updates
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Solution Overview
Problem
Conventional non-maximum suppression (NMS) methods in object detection are limited by their fixed parameter settings, which lead to a tradeoff between precision and recall, especially in crowded scenes, and are not adaptable to data distribution, necessitating a more effective approach for object detection in digital images.
Innovation Solution
A neural network system that generates candidate windows and updates their latent representations based on neighboring windows, allowing for adaptive scoring and suppression of multi-detections, thereby eliminating the need for conventional NMS post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NMS with wide suppression is used, then precision is improved by removing false positives, but recall deteriorates by removing true positives in crowded scenes
Solution Approach 1:
The patent transforms the static, fixed-parameter NMS suppression process into a dynamic, learnable process. The neural network adaptively adjusts suppression behavior based on input data characteristics, allowing the system to automatically narrow suppression in crowded scenes to preserve true positives while maintaining precision in other contexts.
Solution Approach 2:
The patent changes the fixed suppression parameter into a learnable parameter. Instead of using a hand-crafted fixed threshold, the system learns optimal suppression parameters from data, enabling adaptive control of the precision-recall tradeoff based on scene complexity and object density.
2Reliability
If conventional NMS with narrow suppression is used, then recall is improved by preserving close-by true positives, but precision deteriorates by retaining false positives
Solution Approach 1:
The neural network enables dynamic adjustment of suppression strength based on learned patterns. In scenes where false positives are prevalent, the network automatically increases suppression to maintain precision, while in crowded scenes it reduces suppression to preserve recall.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network learns from detection outcomes and adjusts suppression behavior accordingly. The learnable parameters are optimized based on precision-recall performance, creating a feedback loop that continuously improves detection quality.
3Device complexity
If fixed parameter NMS is used, then computational simplicity is maintained, but adaptability to different data distributions deteriorates
Solution Approach 1:
The patent replaces the mechanical, hand-crafted NMS algorithm with a neural network-based system. This substitution transforms the fixed-parameter approach into a learnable model that adapts to different data distributions while maintaining computational efficiency through optimized network architecture.
Solution Approach 2:
The neural network performs self-adjustment of suppression parameters based on the input data characteristics. The system serves itself by automatically learning optimal parameters without requiring manual tuning or external intervention for different datasets or scenarios.
Data Source
AI summary
The invention relates to a system for detecting objects in a digital image. The system comprises a neural network which is configured to generate candidate windows indicating object locations, and to generate for each candidate window a score representing the confidence of detection. Generating the scores comprises:generating a latent representation for each candidate window,updating the latent representation of each candidate window based on the latent representation of neighboring candidate windows, andgenerating the score for each candidate window based on its updated latent representationThe invention further relates to a system for rescoring object detections in a digital image and to methods of detecting objects and rescoring objects.