Multi-view Fine-grained Identification via Active View Selection
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Solution Overview
Problem
Existing fine-grained visual classification methods are limited to single-view pictures, which provide insufficient discriminant features for accurate classification in fine-grained image identification tasks.
Innovation Solution
A multi-view fine-grained identification method that acquires a sample data set with multiple sub-view images from different perspectives, trains an initial fine-grained identification model using these sub-view images, and optimizes the model using an aggregator for feature aggregation and a selector for active view selection until a trained target model is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-view pictures are used for fine-grained visual classification, then the framework is simple, but the classification accuracy deteriorates due to insufficient discriminant features
Solution Approach 1:
The patent transitions from single-view (2D) image classification to multi-view (adding temporal dimension) video-based classification. By incorporating temporal dimension through multiple frames and view selection, the system gains additional discriminative information without significantly increasing structural complexity
Solution Approach 2:
The patent segments the classification task into multiple independent view selections, where each view is selected and classified separately. This allows the system to process complex multi-view data through simpler sequential operations, maintaining manageable framework complexity while improving accuracy
2Productivity
If multi-view samples are processed to improve classification accuracy, then the identification efficiency improves, but the model complexity increases due to aggregator and selector components
Solution Approach 1:
The selector module autonomously determines which views provide the most discriminative information without requiring manual intervention or complex external control systems. The model self-regulates its own processing pipeline by dynamically selecting relevant views based on their informational content
Solution Approach 2:
The patent implements dynamic view selection where the model adapts which views to process based on the specific sample being classified. This dynamic approach allows the system to optimize processing efficiency for each case while managing overall model complexity through flexible, context-dependent operations
3Measurement precision
If all sub-view images are used for training, then the classification accuracy improves, but the training time and computational cost increase
Solution Approach 1:
Instead of processing all available views uniformly, the patent selectively processes only the most informative views identified by the selector module. This partial action approach achieves sufficient classification accuracy by focusing computational resources on critical views rather than exhaustively processing all views
Solution Approach 2:
The patent changes the parameter of view selection from static (processing all views) to dynamic (selective processing based on informational content). By adjusting which views are processed based on their discriminative value, the system optimizes the balance between training time and classification accuracy
Data Source
AI summary
A multi-view fine-grained identification method, apparatus, electronic device and medium. By applying the technical scheme of the application, an initial classification model can be trained by using a sample data set consisting of multi-view images of a plurality of multi-view samples. Thus, an efficient fine-grained identification model can be obtained, and this model can actively select the next view image of the same sample for image identification. On the one hand, by aggregating information of multi-view images of the same sample, the limitation of traditional fine-grained image identification methods that only rely on a single picture to provide clues for discrimination is solved. On the other hand, by predicting view images for discrimination, identification efficiency based on multi-view fine-grained identification is improved.