A research poster from Peking University titled "Distribution-aware Knowledge Prototyping for Lifelong Person Re-Identification." It focuses on mitigating issues like catastrophic forgetting in LReID by employing Distribution-aware Knowledge Prototyping (DKP). The introduction explains the background of the problem and the proposed solution. 

The methodology section includes diagrams illustrating the new approach compared to traditional methods. Three types of prototypes, involving identity feature centers, statistical feature distributions, and instance-level distribution-generated prototypes, are elaborated with visual aid. The points and comments are explained through visual plots and charts. A presenter's hand in a light blue sweatshirt is energetically gesturing towards the diagrams, providing verbal explanations to accompany the visual information. The poster is well-organized with sections clearly marked to guide the audience through the research findings.
Text transcribed from the image:
ING
18
大 北京大學
Distribution-aware Knowledge Prototyping for
Kunlun Xu, Xu Zou², Yu
PEKING UNIVERSITY
Introduction
Wangxuan Institute of Computer Technolog
School of Artificial Intelligence and Automation, Huazhong Unive
Method
from
➤ Lifelong person
catastrophic forgetting when learning continually. Exemplar
re-identification (LReID) suffers
and knowledge distillation-based LReID methods encounter
data privacy and limited acquisition capacity respectively.
Prototype-based works set the prototypes as discrete points or
statistical distributions, either discarding the distribution
information or omitting instance-level diversity which are
crucial fine-grained clues for LReID.
We propose Distribution-aware Knowledge Prototyping (DKP)
where instance-level diversity of each sample is modeled to
transfer comprehensive fine-grained knowledge for prototyping.
Identity Feature
Identity Center
-Step t-1
-Step t-
Dt-1
Pt-1
D₂
(a) Overall Model
X1 X2
Χη
Backbone
Instance center Prototype of step t-1
H
Pooling
H
P-10
PKT
IDM
H
Lproto-di
Lce-d
Liri-di
DPG
P
Conv Linear
Model Mt Instance variance Prototype of step t
Distribution Boundary
(c) Prototype-based Knowledge Transfer (PKT)
91 1
(b) Inst
Insta
New
Identity
Step t-1
(a) Identity feature center A as prototype
New
Identity
Step t-1
(b) Statistical feature distribution (Aas prototype
Step t-1
New
Identity
Step t
HN=-1
Prototype Pt-1
Sampler Prototype sa
➤ Overview: Our model is buil
distribution center and va
IDM and DPG are design
➤IDM: Model the distr
proposed distribut
Step t
➤ PKT: Guide the no
Step t
(c) Instance-level distribution generated prototype (Ours)
A
the aid of historical distribu.
Cp = P(Fc F/2₁)
DPG: Transform the instance-level
Gaussian distribution which is regis
nk
1
14=-
Ci
nk
i=1