An improved interaction-and-aggregation network for person re-identification

Person re-identification (ReID) aims to match a specific person across non-overlapping camera views and has wide application prospects. However, existing methods are still susceptible to occlusion and missing critical parts. Most methods fuse low-level detail features and high-level strong semantic features using feature concatenation or addition, leading to useful information being overwhelmed by a large amount of useless information. In addition, many methods extract spatial context features by designing different blocks but ignore the local channel context features. To relieve these issues, this paper presents an improved interaction-and-aggregation network (IIANet) to learn more representative feature representation. First, to improve model robustness to serious occlusion or missing crucial parts of the target person, we employ a global multi-scale module (MSM) to extract multi-scale features by multi-branch convolution and hierarchical residual connection. Second, to selectively fuse low-level detail features and high-level semantic features effectively, we design a gated fully fusion module (GFFM) to control information transmission and reduce feature interferences in fusing different-level features. Finally, we adopt a channel context module (CCM) to learn channel context information via multi-scale local fusion. Sufficient experiments demonstrate the better performances of our IIANet on dataset Market-1501. The mAP and Rank-1 accuracy of our model reach 84.9% and 94.2%, respectively. Our code is available at: https://gitee.com/bingsfan/iianet/tree/master/