- ½¹µãÊÖÒÕ
- ÒÔÔ´´ÊÖÒÕϵͳΪ»ù±¾£¬£¬£¬£¬£¬SenseCoreÉÌÌÀAI´ó×°ÖÃΪ½¹µã»ù×ù£¬£¬£¬£¬£¬½á¹¹¶àÁìÓò¡¢¶àÆ«ÏòÇ°ÑØÑо¿£¬£¬£¬£¬£¬
¿ìËÙÂòͨAIÔÚ¸÷¸ö±ÊÖ±³¡¾°ÖеÄÓ¦Ó㬣¬£¬£¬£¬ÏòÐÐÒµ¸³ÄÜ¡£¡£¡£¡£¡£¡£
CVPR 2020 | HFE: ¶à²ã¼¶µÄÌØÕ÷¹¹½¨·½·¨½â¶Á

µ¼¶Á£ºÔÚCVPR 2020ÉÏ£¬£¬£¬£¬£¬ÉÌÌÀ¶¼»áÅÌËãÍŶÓÌá³öÁËÒ»¸öÕë¶ÔÊôÐÔ·ÖÀàµÄ¶àÌõÀíÌØÕ÷µÄ¹¹½¨·½·¨£¬£¬£¬£¬£¬¼´Ê¹ÓÃÉí·Ý(ID)ÐÅÏ¢¸¨Öú¹¹½¨¶àÌõÀíµÄÌØÕ÷£¬£¬£¬£¬£¬¸ÃÊÂÇéÎªÌØÕ÷¹¹½¨·½·¨ÌṩÁËеÄ˼Ð÷¡£¡£¡£¡£¡£¡£Ïà±ÈÓÚÒÑÓеÄÊôÐÔ·ÖÀàÒªÁ죬£¬£¬£¬£¬HFEÔÚÌØÕ÷ÌåÏÖºÍÐÔÄÜÖ¸±êÉϾùÓÐÏÔÖøÓÅÊÆ¡£¡£¡£¡£¡£¡£
ÄîÍ·
Ö±½ÓʹÓÃCross Entropy LossѵÁ·µÄ¾í»ýÉñ¾ÍøÂç·ÖÀ࣬£¬£¬£¬£¬Ö»ÊǰÑͳһÀàµÄÌØÕ÷¾Û£µ½Ò»Æð£¬£¬£¬£¬£¬¹ØÓÚÀàÄÚûÓÐÔ¼Êø¡£¡£¡£¡£¡£¡£ÎÒÃÇÔÚ´øÓÐIDÐÅÏ¢µÄÊôÐÔÊý¾Ý¼¯ÉϾÙÐÐʵÑ飬£¬£¬£¬£¬Ö»Ê¹ÓÃÊôÐÔ±êÇ©¾ÙÐзÖÀàʱ£¬£¬£¬£¬£¬·¢Ã÷ÀàÄÚÂþÑܺÜÊÇÔÓÂÒ£¬£¬£¬£¬£¬Í³Ò»¸öIDµÄͼƬ»áÊèÉ¢ÔÚÀàÄڵĸ÷¸öλÖᣡ£¡£¡£¡£¡£Õâ˵Ã÷ֻʹÓÃÊôÐÔ±êǩѵÁ·µÄ·ÖÀàÍøÂ磬£¬£¬£¬£¬ÎÞ·¨×¼È·µÄ½« ID ÏàͬµÄͼƬÔÚÌØÕ÷¿Õ¼äÉÏÓ³Éäµ½×ã¹»½üµÄ¾àÀ룬£¬£¬£¬£¬Òò´ËÆäÌØÕ÷±í´ï²»·óÎȹ̣¬£¬£¬£¬£¬ÈÝÒ×ÊܼÓÈ뾰ת±äµÈÒòËØµÄ×ÌÈÅ£¬£¬£¬£¬£¬ÔÚÊäÈ뱬·¢×ª±äʱ£¬£¬£¬£¬£¬ÌØÕ÷»á±¬·¢½ÏÁ¿´óµÄÆ«ÒÆ£¬£¬£¬£¬£¬ÔöÌíÁË·ÖÀà¹ýʧµÄ¿ÉÄÜÐÔ¡£¡£¡£¡£¡£¡£¹þ¹þ(haha)ÌåÓýÒªÁìÔÚ´Ë´¦×ö³öˢУ¬£¬£¬£¬£¬Ê¹ÓÃIDºÍÊôÐÔ±êÇ©×÷ΪÁ½¸ö²ã¼¶µÄ¼àÊÓ£¬£¬£¬£¬£¬ÔÚÌØÕ÷¿Õ¼äÀï¹¹½¨¶à²ã¼¶µÄÂß¼½á¹¹£¬£¬£¬£¬£¬ÒÔÌá¸ßÄ£×ӵıíÕ÷ÄÜÁ¦¡£¡£¡£¡£¡£¡£
¸ÃÊÂÇéµÄÑо¿ÄîÍ·ÔÚÓÚ£º
1. ʹÓÃÉí·ÝÐÅÏ¢À´ÏÞÖÆÍ³Ò»Ð¡ÎÒ˽¼ÒµÄ²î±ð³¡¾°¡¢²î±ð½Ç¶È¡¢²î±ð×Ë̬µÄÑù±¾ÌØÕ÷Ⱥ¼¯µØ¸üϸÃÜ£¬£¬£¬£¬£¬Ê¹ÌØÕ÷¹ØÓÚ³¡¾°¡¢½Ç¶È¡¢×Ë̬µÈµÄת±äØÔ³°ô¡£¡£¡£¡£¡£¡£
2. ͨ¹ýÉí·ÝµÄÏÞÖÆ£¬£¬£¬£¬£¬ÊôÓÚͳһ¸öIDµÄ¼òÆÓÑù±¾¿ÉÒÔ°ÑÔÀ´ÄÑѧµÄÄÑÌâÑù±¾ÔÚÌØÕ÷¿Õ¼äÉÏÀ½ü£¬£¬£¬£¬£¬´Ó¶øÊ¹ÄÑÌâÑù±¾¸üÈÝÒ×ѧϰ¡£¡£¡£¡£¡£¡£
3. ʹÓÃÉí·ÝµÄÐÅÏ¢À´½á¹¹ÌõÀíÐÔµÄÊôÐÔÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬×èÖ¹°ÑÁ½ÖÖÌØÕ÷¼òÆÓµØÇ¶Èëµ½Í³Ò»ÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬¶øÊÇͳһµ½ÊôÐÔµÄÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬Ê¹½á¹¹µÄÌØÕ÷¿Õ¼ä¸üºÏÀí¡£¡£¡£¡£¡£¡£
ÈçÏÂͼËùʾ£º

ÒªÁìÉè¼Æ
ÎÒÃÇÌáµÄÒªÁìÕûÌå¿ò¼ÜÈçÏÂͼ£¬£¬£¬£¬£¬Ö÷ÒªÓÉÒ»¸öÖ÷¸ÉÍøÂ磬£¬£¬£¬£¬¼Ó¶à¸öÊôÐÔ·ÖÖ§×é³É¡£¡£¡£¡£¡£¡£¹²ÏíµÄÖ÷¸ÉÍøÂçÓÃÀ´Ñ§Ï°ËùÓÐÊôÐÔµÄÅäºÏÌØÕ÷£¬£¬£¬£¬£¬¶øÃ¿¸öµ¥¶ÀµÄÊôÐÔ·ÖÖ§ÓÃÀ´Ñ§Ï°¸÷¸öÊôÐÔ¸÷×ÔµÄÌØÕ÷¡£¡£¡£¡£¡£¡£

¹ØÓÚËðʧº¯Êý£¬£¬£¬£¬£¬Ê×ÏÈÎÒÃÇÑØÓùŰåµÄÒªÁ죬£¬£¬£¬£¬½ÓÄɽ»Ö¯ìØ£¨Cross Entropy, CE£©Ëðʧº¯Êý¾ÙÐÐÊôÐÔ·ÖÀà¡£¡£¡£¡£¡£¡£

ÕâÀïN´ú±íͼƬÑù±¾¸öÊý£¬£¬£¬£¬£¬M´ú±íÊôÐÔ¸öÊý£¬£¬£¬£¬£¬
´ú±íµÚi¸öÑù±¾µÄµÚj¸öÊôÐԵıêÇ©£¬£¬£¬£¬£¬
´ú±í¶ÔµÚi¸öÑù±¾µÚj¸öÊôÐÔµÄÕ¹Íû¸ÅÂÊ¡£¡£¡£¡£¡£¡£
ÔÚCE»ù´¡ÉÏÍŽáÎÒÃÇÉè¼ÆµÄ¶àÌõÀíÌØÕ÷Ëðʧº¯Êý£¬£¬£¬£¬£¬×ܵÄËðʧº¯Êý¿ÉÒÔд³ÉÈçÏÂËùʾ¡£¡£¡£¡£¡£¡£ÕâÀïwÌåÏÖÈ¨ÖØ¡£¡£¡£¡£¡£¡£
![]()
Ò». ¶àÌõÀíÌØÕ÷Ëðʧº¯Êý
¶àÌõÀíÌØÕ÷Ëðʧº¯ÊýÖ÷ÒªÓÉÁ½¸öÈýÔª×é×é³É£¬£¬£¬£¬£¬Ò»¸öÊǹŰåµÄÀà¼äÈýÔª×éÈçÏ¡£¡£¡£¡£¡£¡£

ÆäÖÐ
ÌåÏÖÑù±¾iµÄjÊôÐÔµÄÌØÕ÷£¬£¬£¬£¬£¬³ÆÎªÃªÑù±¾¡£¡£¡£¡£¡£¡£
ÌåÏÖºÍêÑù±¾
ͬÊôÒ»¸öÊôÐÔÖÖ±ðµ«ÀëêÑù±¾×îÔ¶µÄÕýÑù±¾µÄÌØÕ÷¡£¡£¡£¡£¡£¡£
ÌåÏÖºÍêÑù±¾
²»ÊôÓÚͳһ¸öÊôÐÔÖÖ±ðµ«ÀëêÑù±¾×î½üµÄ¸ºÑù±¾µÄÌØÕ÷¡£¡£¡£¡£¡£¡£
ÌåÏÖÀà¼ä²î¶î£¬£¬£¬£¬£¬
ÌåÏÖÑù±¾iµÄÉí·Ý£¬£¬£¬£¬£¬¶ød()ÌåÏÖÁ½¸öÑù±¾µÄ¾àÀë¡£¡£¡£¡£¡£¡£
ΪÁËÐγÉϸÁ£¶ÈµÄ¶àÌõÀíµÄÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬ÎÒÃÇʹÓÃÉí·ÝÐÅÏ¢¹¹½¨ÀàÄÚÈýÔª×飬£¬£¬£¬£¬ÈçÏ¡£¡£¡£¡£¡£¡£

͉˕
ÌåÏÖºÍêÑù±¾
ͬÊôÓÚÒ»¸öÊôÐÔÖÖ±ðҲͬÊôÓÚÒ»¸öÉí·ÝµÄÀëêÑù±¾×îÔ¶µÄÕýÑù±¾µÄÌØÕ÷¡£¡£¡£¡£¡£¡£
ÌåÏÖºÍêÑù±¾
ͬÊôÒ»¸öÊôÐÔÖֱ𵫲»ÊôÓÚͳһ¸öÉí·ÝµÄÀëêÑù±¾×î½üµÄÕýÑù±¾µÄÌØÕ÷¡£¡£¡£¡£¡£¡£
ÌåÏÖÀàÄÚ²î¶î¡£¡£¡£¡£¡£¡£
ÍŽáÉÏÊöµÄÁ½¸öloss£¬£¬£¬£¬£¬ÎÒÃÇ¿ÉÒÔͬʱά»¤Àà¼äºÍÀàÄÚµÄÌØÕ÷¿Õ¼ä¡£¡£¡£¡£¡£¡£ÈçÏÂͼËùʾ£¬£¬£¬£¬£¬Í¨¹ýÎåÔª×éµÄÏÞÖÆ£¬£¬£¬£¬£¬ÎÒÃÇ¿ÉÒÔά»¤Ò»¸ö¶à²ã¼¶µÄÏà¶Ô¾àÀ룬£¬£¬£¬£¬´Ó¶øµÖ´ï¹¹½¨ÌõÀí»¯µÄÌØÕ÷¿Õ¼äµÄÄ¿µÄ¡£¡£¡£¡£¡£¡£

¶þ. ¾ø¶Ô½çÏßÕýÔòÏî
ÉÏÊöµÄËðʧº¯ÊýËäÈ»ÄÜͬʱά»¤Àà¼äÌØÕ÷ºÍÀàÄÚÌØÕ÷£¬£¬£¬£¬£¬¿ÉÊÇֻ˼Á¿ÁËÏà¶Ô¾àÀë¡£¡£¡£¡£¡£¡£´Ó¾ø¶Ô¾àÀë½Ç¶ÈÉÏ¿´£¬£¬£¬£¬£¬²¢²»¿É°ü¹ÜÔÚÕû¸öѵÁ·¼¯ÖУ¬£¬£¬£¬£¬ÃªÑù±¾ºÍÕýÑù±¾µÄ¾àÀ붼СÓÚêÑù±¾ºÍ¸ºÑù±¾µÄ¾àÀë¡£¡£¡£¡£¡£¡£Îª´Ë£¬£¬£¬£¬£¬ÎÒÃÇÉè¼ÆÁ˾ø¶Ô½çÏßÕýÔòÏAbsolute Boundary Regularization£¬£¬£¬£¬£¬ABR£©ÈçÏ£º

Òò´Ë¹þ¹þ(haha)ÌåÓý¶àÌõÀíÌØÕ÷Ëðʧº¯ÊýÓÉÉÏÊöÈýÏî×é³É¡£¡£¡£¡£¡£¡£

Èý. ¶¯Ì¬È¨ÖØ
ÔÚѵÁ·³õʼµÄʱ¼ä»ñµÃµÄÌØÕ÷¿Õ¼ä²¢²»¿É¿¿£¬£¬£¬£¬£¬ÓÉÓÚÎåÔª×éµÄÑ¡ÔñÒÀÀµÓÚÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬ÈôÊÇÒ»×îÏȾÍÓýϴóµÄÈ¨ÖØ£¬£¬£¬£¬£¬¿ÉÄÜ»á´øÀ´ÔëÒô¡£¡£¡£¡£¡£¡£ÒÔÊÇÎÒÃÇΪÉÏÊöËðʧº¯ÊýÉè¼ÆÁËÒ»¸ö¶¯Ì¬È¨ÖØ£¬£¬£¬£¬£¬Í¨¹ýÈ¨ÖØ¶¯Ì¬Ôö´ó£¬£¬£¬£¬£¬Ê¹ÌØÕ÷¿Õ¼äÖ𲽵شÓÔʼµÄ״̬תÏòÌõÀí»¯µÄ״̬¡£¡£¡£¡£¡£¡£

ÕâÀïTÌåÏÖÕû¸öѵÁ·µÄµü´ú´ÎÊý£¬£¬£¬£¬£¬¶øiterÌåÏÖÄ¿½ñµÄµü´ú´ÎÊý¡£¡£¡£¡£¡£¡£w0ÊÇÒ»¸öÔ¤ÏÈÉèÖúõij£Êý¡£¡£¡£¡£¡£¡£
ʵÑéЧ¹û
±¾ÎÄʹÓÃÁËÁ½¸öÐÐÈËÊôÐÔÊý¾Ý¼¯£ºMarket 1501[1]ºÍDuke[2]£¬£¬£¬£¬£¬ºÍһСÎÒ˽¼ÒÁ³ÊôÐÔÊý¾Ý¼¯£ºCelebA[3]£¬£¬£¬£¬£¬¾ÙÐÐÁËʵÑé¡£¡£¡£¡£¡£¡£ÔÚÈý¸öÊý¾Ý¼¯ÉϵÄʵÑéЧ¹ûÅú×¢£¬£¬£¬£¬£¬HFE±ÈÏÖÓеÄ×îÏȽøµÄÒªÁì¸ü¾ß¾ºÕùÁ¦£¬£¬£¬£¬£¬ÈçϱíËùʾ£º



ΪÁ˽øÒ»²½ÆÊÎö¸÷¸ö×é¼þµÄЧ¹û£¬£¬£¬£¬£¬ÎÒÃÇÔÚmarket 1501ÉÏ×öÁËÏ꾡µÄ±ÈÕÕʵÑ飬£¬£¬£¬£¬ÈçϱíËùʾ£¬£¬£¬£¬£¬¿ÉÒÔ¿´µ½Ã¿¸ö²¿·Ö¶¼ÊÇÓи÷×ÔµÄÌáÉýЧ¹ûµÄ¡£¡£¡£¡£¡£¡£

ΪÁËÖ±¹ÛµØÊÓ²ìÌØÕ÷¿Õ¼äµÄת±ä£¬£¬£¬£¬£¬ÎÒÃǶÔÒ»¸öÊôÐÔÔÚ²î±ðlossϵÄÌØÕ÷¿Õ¼ä¾ÙÐÐÁË¿ÉÊÓ»¯£¬£¬£¬£¬£¬ÈçÏÂͼËùʾ¡£¡£¡£¡£¡£¡£¿£¿£¿£¿£¿£¿£¿ÉÒÔ¿´µ½£¬£¬£¬£¬£¬Ã¿ÔöÌíÒ»¸ö×é¼þ£¬£¬£¬£¬£¬ÀàÄÚµÄÌØÕ÷¶¼¸üϸÃÜ£¬£¬£¬£¬£¬¶øÀà¼äµÄÌØÕ÷¾àÀë¸üÔ¶£¬£¬£¬£¬£¬½çÏ߸üÇåÎú¡£¡£¡£¡£¡£¡£²¢ÇÒHFEȷʵ¿ÉÒÔÐγɸüϸÁ£¶ÈµÄÀàÄÚÌØÕ÷¿Õ¼ä£¬£¬£¬£¬£¬Í¬Ê±ÈÃÀàÄÚ¸ü½ô´Õ£¬£¬£¬£¬£¬Àà¼ä¸üÇåÎú£¬£¬£¬£¬£¬Öª×ã¹þ¹þ(haha)ÌåÓýÔ¤ÆÚ¡£¡£¡£¡£¡£¡£

ÏÂͼΪÊôÐÔ¿ÉÊÓЧ¹û±ÈÕÕ£¬£¬£¬£¬£¬¿ÉÒÔ¿´³ö£¬£¬£¬£¬£¬¹ØÓÚÊôÐÔ½ÏÁ¿ÇåÎú¿É¼ûʱ£¬£¬£¬£¬£¬Èý¸öÒªÁì¶¼ÄÜÅжÏ׼ȷ£»£»£»£»£»£»£»¶øµ±ÕÚµ²±¬·¢»òͼÏñÄ£ºýʱ£¬£¬£¬£¬£¬CEºÍAPR[4]·ºÆð¹ýʧչÍûµÄ¸ÅÂʽϸߣ¬£¬£¬£¬£¬HFEÈ´ÕÕ¾ÉÄÜÕ¹Íû׼ȷ¡£¡£¡£¡£¡£¡£

´«ËÍÃÅ
ÂÛÎĵص㣺
https://arxiv.org/abs/2005.11576
½Ó´ý¸ÐÐËȤµÄÅóÙÔĶÁºÍ½»Á÷¡£¡£¡£¡£¡£¡£
References
[1] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jing- dong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international con- ference on computer vision, pages 1116¨C1124, 2015.
[2] ZhedongZheng,LiangZheng,andYiYang.Unlabeledsam- ples generated by gan improve the person re-identification baseline in vitro. In Proceedings of the IEEE International Conference on Computer Vision, pages 3754¨C3762, 2017.
[3] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730¨C3738, 2015.
[4] Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhi- lan Hu, Chenggang Yan, and Yi Yang. Improving person re-identification by attribute and identity learning. Pattern Recognition, 2019.





·µ»Ø