TY - JOUR AU - Chen, Yun-Chun AU - Li, Yu-Jhe AU - Du, Xiaofei AU - Frank Wang, Yu-Chiang PY - 2019/07/17 Y2 - 2024/03/29 TI - Learning Resolution-Invariant Deep Representations for Person Re-Identification JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v33i01.33018215 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4832 SP - 8215-8222 AB - <p>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.</p> ER -