Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition

• URL: http://arxiv.org/abs/2409.11051v1
• Date: Tue, 17 Sep 2024 10:17:34 GMT
• Title: Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition
• Authors: Edwin Arkel Rios, Femiloye Oyerinde, Min-Chun Hu, Bo-Cheng Lai,
• Abstract summary: We introduce a novel approach employing down-sampling inter-layer adapters in a parameter-efficient setting. By integrating dual-branch down-sampling, we significantly reduce the number of parameters and floating-point operations required. Our method increases the average accuracy by at least 6.8% compared to other methods in the parameter-efficient setting.
• Score: 5.332719186390523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultra-fine-grained image recognition (UFGIR) categorizes objects with extremely small differences between classes, such as distinguishing between cultivars within the same species, as opposed to species-level classification in fine-grained image recognition (FGIR). The difficulty of this task is exacerbated due to the scarcity of samples per category. To tackle these challenges we introduce a novel approach employing down-sampling inter-layer adapters in a parameter-efficient setting, where the backbone parameters are frozen and we only fine-tune a small set of additional modules. By integrating dual-branch down-sampling, we significantly reduce the number of parameters and floating-point operations (FLOPs) required, making our method highly efficient. Comprehensive experiments on ten datasets demonstrate that our approach obtains outstanding accuracy-cost performance, highlighting its potential for practical applications in resource-constrained environments. In particular, our method increases the average accuracy by at least 6.8\% compared to other methods in the parameter-efficient setting while requiring at least 123x less trainable parameters compared to current state-of-the-art UFGIR methods and reducing the FLOPs by 30\% in average compared to other methods.

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