Two individuals stand intently studying an academic poster displayed in a conference setting. The poster, titled "Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification," presents a detailed scientific research study. It includes blocks of text, methodological flowcharts, and visual data representations. The setting features modern overhead lighting and a structured ceiling design, characteristic of a professional conference or symposium venue. The individuals, seen from the back, appear deeply engaged with the content, suggesting a high level of interest or involvement in the topic being presented.
Text transcribed from the image:
Featur
Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification
Zhenyu Cui¹, Jiahuan Zhou, Xun Wang2, Manyu Zhu, Yuxin Peng1*
1Wangxuan Institute of Computer Technology, Peking University 2Byte Dance Inc
(L-ReID): Aims to learn from
a person across different
orical images in the gallery
testing (Re-indexing).
xing
Method:
Overview: A Continual Compatible Representation (C2R) scheme is
proposed for RFL-ReID to continuously update old features in the
gallery to make it compatible with new query features.
space!
Continual Compatible Transfer (CCT): Update old gallery
features continuously and transfer old feature to the new feature
space, adaptively capturing the knowledge from different domains.
Balanced Compatible Distillation (BCD): Achieve the
compatibility between the transferred features and the new ones,
preserving the relationship between the old and the transferred
features in a unified feature space.
Balanced Anti-forgetting Distillation (BAD): Eliminate the
accumulated forgetting of old knowledge during the continuous
transfer, balancing the old and the new discriminative information.
Canal Compatible Transfer CCT
Experiments:
The experiment on the benchmark L-R
superiority of our proposed C2R.
Method
Market-1501 CUHK-SYSU DeMTMC-ReD
RUI
MAP
Joint Train
SPD 125)
68.1 852 814
356 612 61.7
83.3 604
LwF[i]
563 77.1 729
296
CRL 140
580
782
72.5
75.1
28.3
L-RelD
AKA (1) 58.1
74.
28.7
390 616
733
76.6 165
RFL-RelD
MEGE [201
PatchKD 1271 685 85.7 75.6 28
69.0 36.X
76,7 "
LF)
580 400
39.1
522
36.1
AKA [18]
366
38% 55.6 49.0
CVS [301
784 37
PatchKD (271) 614
Ours 627 79.7
Our C2R achieves stable
anced
out re-
Hin
Conclusion
We propose C2R for the pra
tackling the data privacy issu
Two balanced distillation
forgetting of old knowledge
Extensive experiments
demonstrate the effectiven
L
Pipeline:
More
Training Stage: CCT, BCD, and BAD are jointly optimized.
Transferring Stage: CCT network is employed to update the old
feature set after each training stage in L-ReID.
Information
Go!
Testing Stage: Query features calculated by the new model is
used to directly match the updated gallery features to achieve re
indexing free lifelong person re-identification.
Three stages are executed sequentially when facing new dat
Related