Incremental Learning in Person Re-identification

Person Re-Identification is still a challenging task in Computer Vision due to variety of reasons. On the other side, Incremental Learning is still an issue since Deep Learning models tend to face the problem of overcatastrophic forgetting when trained on subsequent tasks. In this paper, we propose a model which can be used for multiple tasks in Person Re-Identification, provide state-of-the-art results on variety of tasks and still achieve considerable accuracy later on. We evaluated our model on two datasets Market 1501 and Duke MTMC. Extensive experiments show that this method can achieve Incremental Learning in Person ReID efficiently as well as for other tasks in computer vision as well.

  • Achieved 89.3% (Rank-1) accuracy and mAP of 71.8 on Market 1501
  • Achieved 80.0% (Rank-1) accuracy and mAP of 60.2 on Duke MTMC
  • Code Paper