Jinjia Peng
14 papers · 2018–2026 · 5 venues · across top CS/AI venues
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Achievements
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Keyword Collector
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Research topics
Machine Learning
(12)
Learning Types Β· Machine Learning
(10)
Computer Vision
(8)
Application Areas
(7)
Analysis
(6)
Deep Learning
(5)
Learning Types Β· Deep Learning
(5)
Core Methods
(4)
Artificial Intelligence
(2)
Core AI
(2)
Generation
(2)
Optimization & Theory
(2)
Data Science & Analytics
(1)
Applications
(1)
Architectures
(1)
Keywords
domain adaptation
(6)
person re-identification
(4)
unsupervised learning
(4)
person search
(4)
contrastive learning
(3)
unsupervised domain adaptation
(3)
object detection
(3)
feature alignment
(2)
pseudo label
(2)
multi-view clustering
(2)
adversarial example
(2)
loss landscape
(2)
domain generalization
(1)
image synthesis
(1)
attention mechanism
(1)
image generation
(1)
style transfer
(1)
posterior sampling
(1)
semantic consistency
(1)
pseudo labeling
(1)
Papers
Dynamic Magic: Unleashing Restricted Knowledge for Lifelong Person Re-Identification
CVPR 2026
Instance-Guided Scene Adaptation for Unsupervised Person Search
AAAI 2026
Localization-Anchored Instance Discrimination for Domain Adaptive Person Search
AAAI 2026
Prompting Adversarial Transferability via Path Flatness Attack
AAAI 2026
Anchor Learning with Potential Cluster Constraints for Multi-view Clustering
AAAI 2025
Boosting Adversarial Transferability via Residual Perturbation Attack
ICCV 2025
CDE-Learning: Camera Deviation Elimination Learning for Unsupervised Person Re-identification
AAAI 2025
Consensus-Guided Incomplete Multi-view Clustering via Cross-view Affinities Learning
IJCAI 2025
Stabilizing Holistic Semantics in Diffusion Bridge for Image Inpainting
IJCAI 2025
Unsupervised Domain Adaptive Person Search via Dual Self-Calibration
AAAI 2025
Fast One-Stage Unsupervised Domain Adaptive Person Search
IJCAI 2024
Scene-Adaptive Person Search via Bilateral Modulations
IJCAI 2024
Unsupervised Vehicle Re-identification with Progressive Adaptation
IJCAI 2020
Refining Synthetic Images with Semantic Layouts by Adversarial Training
ACML 2018