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Found 6 papers, 1 papers with code
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.
Knowledge Distillation
Unsupervised Person Re-Identification
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL).
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation
Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states.
Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.
Domain Adaptive Person Re-Identification
Ensemble Learning
+1
A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification
RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities.
Cross-Modality Person Re-identification
Person Re-Identification
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown.
Ranked #8 on
Unsupervised Domain Adaptation
on Duke to Market
