Learning Resolution-Invariant Deep Representations for Person Re-Identification


  • Yun-Chun Chen National Taiwan University
  • Yu-Jhe Li National Taiwan University
  • Xiaofei Du Umbo Computer Vision
  • Yu-Chiang Frank Wang National Taiwan University




Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios might suffer from resolution loss, how to solve the resolution mismatch problem during person re-ID becomes a practical problem. Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy of adversarial learning, we aim at extracting resolution-invariant representations for re-ID, while the proposed model is learned in an end-to-end training fashion. Our experiments confirm that the use of our model can recognize low-resolution query images, even if the resolution is not seen during training. Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.




How to Cite

Chen, Y.-C., Li, Y.-J., Du, X., & Frank Wang, Y.-C. (2019). Learning Resolution-Invariant Deep Representations for Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8215-8222. https://doi.org/10.1609/aaai.v33i01.33018215



AAAI Technical Track: Vision