计算机应用与软件2024，Vol.41Issue(12)：182-187,246,7.DOI:10.3969/j.issn.1000-386x.2024.12.026
基于自适应步幅卷积的细粒度视觉识别
FINE-GRAINED VISUAL CLASSIFICATION BASED ON ADAPTIVE STRIDE CONVOLUTION
摘要
Abstract
Down-sampling methods such as average pooling have been widely used to reduce computation cost,prevent overfitting,and improve the performance of convolutional neural networks.However,in fine-grained recognition tasks,these uniform sampling methods cannot focus well on subtle discriminative regions.In this paper,we propose an Adaptive Stride Convolution Network(ASCNet)in which the ASC module is used to focus on extracting subtle features.Specifically,given an image,we obtained an attention map to highlight the discriminative parts of object,where the attention map extractor was used.The attention map-based stride generator produced stride vectors which indicated the moving steps of convolutional kernels every time.The adaptive stride convolution extracted information over the input image or features with varying strides.We experimentally evaluated the effectiveness of our method on three challenging fine-grained benchmarks,i.e.,CUB-200-2011,Stanford Cars,and FGVC-Aircraft,and advanced performance is achieved.关键词
细粒度视觉识别/注意力机制/卷积/下采样/计算机视觉Key words
Fine-grained visual classification/Attention mechanism/Convolution/Down-sampling/Computer vision分类
信息技术与安全科学引用本文复制引用
谢毓广,容圣海,高博,丁津津,王子磊..基于自适应步幅卷积的细粒度视觉识别[J].计算机应用与软件,2024,41(12):182-187,246,7.基金项目
国网安徽省电力有限公司科技项目(B31205200009). （B31205200009）
