Abstract
Person Re-identification (ReID) aims to retrieve the specific person across non-overlapping cameras, which greatly helps intelligent transportation systems. As we all know, Convolutional Neural Networks (CNNs) and Transformers have the unique strengths to extract local and global features, respectively. Considering this fact, we focus on the mutual fusion between them to learn more comprehensive representations for persons. In particular, we utilize the complementary integration of deep features from different model structures. We propose a novel fusion framework called FusionReID to unify the strengths of CNNs and Transformers for image-based person ReID. More specifically, we first deploy a Dual-branch Feature Extraction (DFE) to extract features through CNNs and Transformers from a single image. Moreover, we design a novel Dual-attention Mutual Fusion (DMF) to achieve sufficient feature fusions. The DMF comprises Local Refinement Units (LRU) and Heterogenous Transmission Modules (HTM). LRU utilizes depth-separable convolutions to align deep features in channel dimensions and spatial sizes. HTM consists of a Shared Encoding Unit (SEU) and two Mutual Fusion Units (MFU). Through the continuous stacking of HTM, deep features after LRU are repeatedly utilized to generate more discriminative features. Extensive experiments on three public ReID benchmarks demonstrate that our method can attain superior performances than most state-of-the-arts. The source code is available at this https URL.
Abstract (translated)
人员再识别（ReID）的目标是在非重叠摄像头之间检索特定的人，这极大地帮助了智能交通系统。众所周知，卷积神经网络（CNNs）和Transformer分别在提取局部特征和全局特征方面具有独特的优势。考虑到这一点，我们专注于它们之间的相互融合以学习更全面的人员表示。具体而言，我们利用不同模型结构中深层特征的互补整合。我们提出了一种名为FusionReID的新颖融合框架，旨在统一CNNs和Transformer的优势，用于基于图像的人员再识别。更具体地说，我们首先部署了双分支特征提取（DFE）通过CNNs和Transformers从单个图像中提取特征。此外，我们设计了一种新颖的双重注意力相互融合（DMF），以实现充分的特征融合。DMF包括局部精化单元（LRU）和异构传输模块（HTM）。LRU利用深度可分离卷积在通道维度和空间尺寸上对齐深层特征。HTM由共享编码单元（SEU）和两个相互融合单元（MFU）组成。通过持续堆叠HTM，经过LRU处理后的深层特征被反复用于生成更具判别力的特征。我们在三个公共ReID基准数据集上的广泛实验表明，我们的方法可以达到优于大多数最新方法的表现。源代码可在该网址获取：[此链接]。
URL
https://arxiv.org/abs/2412.17239
