通过下采样适配器实现高效超细粒度图像识别
Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition
- 在冻结主干网络前提下，仅微调少量下采样适配模块
- 相比当前方法参数减少123倍，浮点运算量降低30%
- 平均准确率提升至少6.8%，适合资源受限场景
超细粒度图像识别（UFGIR）旨在区分同一物种内差异极小的类别（如不同品种），其挑战在于每类样本稀缺。本文提出一种基于下采样跨层适配器的新方法，在参数高效设置下冻结主干网络，仅微调少量附加模块。通过引入双分支下采样机制，显著降低参数量与浮点运算量（FLOPs）。在十个数据集上的实验证明，该方法在参数效率与精度之间取得优异平衡，相比现有参数高效方法平均准确率提升至少6.8%，可训练参数减少至少123倍，平均浮点运算量降低30%，具备在资源受限环境中的实际应用潜力。
原文摘要 · Abstract (English)
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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