Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition

Highlights：

• We propose a minimum spanning tree aggregation module to aggregate context information for each pixel by utilizing the structural characteristic of minimum spanning tree, which can help the fine-grained discriminative information restoration sub-network keep a watchful eye on discriminative fine-grained details.

• We introduce a semantic relation distillation loss to help the recognition sub-network calibrate the relationship between features, which further prompts the fine-grained detail restoration sub-network to generate the unambiguous details of super-resolution images and recognition sub-network to be aware of discriminative features.

• Extensive experiments are carried out on four challenging datasets (CUB-200-2011, Stanford Cars, FGVC-Aircraft and RP-281) to demonstrate the effectiveness of our framework.

摘要

•To the best of our knowledge, we are the first to address the issue of weakly supervised low-resolution fine-grained image recognition in an end-to-end manner. By enhancing the network’s perception of discriminative features, the necessary critical details are recovered for fine-grained recognition, so as to improve the performance of weakly supervised low-resolution fine-grained image recognition.•We propose a minimum spanning tree aggregation module to aggregate context information for each pixel by utilizing the structural characteristic of minimum spanning tree, which can help the fine-grained discriminative information restoration sub-network keep a watchful eye on discriminative fine-grained details.•We introduce a semantic relation distillation loss to help the recognition sub-network calibrate the relationship between features, which further prompts the fine-grained detail restoration sub-network to generate the unambiguous details of super-resolution images and recognition sub-network to be aware of discriminative features.•Extensive experiments are carried out on four challenging datasets (CUB-200-2011, Stanford Cars, FGVC-Aircraft and RP-281) to demonstrate the effectiveness of our framework.