Greedy $k$-Center From Noisy Distance Samples

Jali, Neharika; Karamchandani, Nikhil; Moharir, Sharayu

doi:10.1109/tsipn.2022.3164352

Cited by 3 publications

(1 citation statement)

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“…We define that given a number of features in a metric space, we wish to find a subset of k features such that the minimum distance between any two features within the subset is maximized. However, this problem is known to be NP-hard [21], making it unsuitable for our real-world applications. R 2 MMT instead uses a greedy implementation of the algorithm proven to be Ω(log k)-competitive with the optimal solution while proving to be significantly faster, especially for larger sets of data [1].…”

Section: Online Unsupervised Domain Adaptationmentioning

confidence: 99%

Real-time online unsupervised domain adaptation for real-world person re-identification

Neff,

Danesh Pazho,

Tabkhi

2023

J Real-Time Image Proc

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Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R 2 OUDA) for Person Re-identification. The R 2 OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R 2 OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R 2 MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R 2 MMT was used to construct over 100 data subsets and train more than 3000 models, exploring the breadth of the R 2 OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world applications.

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Section: Online Unsupervised Domain Adaptationmentioning

confidence: 99%

Real-time online unsupervised domain adaptation for real-world person re-identification

Neff,

Danesh Pazho,

Tabkhi

2023

J Real-Time Image Proc

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Greedy $k$-Center from Noisy Distance Samples

Jali

Karamchandani

Moharir

2021

2021 IEEE International Symposium on Information Theory (ISIT)

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We study a variant of the canonical k-center problem over a set of vertices in a metric space, where the underlying distances are apriori unknown. Instead, we can query an oracle which provides noisy/incomplete estimates of the distance between any pair of vertices. We consider two oracle models: Dimension Sampling where each query to the oracle returns the distance between a pair of points in one dimension; and Noisy Distance Sampling where the oracle returns the true distance corrupted by noise. We propose active algorithms, based on ideas such as UCB and Thompson sampling developed in the closely related Multi-Armed Bandit problem, which adaptively decide which queries to send to the oracle and are able to solve the k-center problem within an approximation ratio of two with high probability. We analytically characterize instance-dependent query complexity of our algorithms and also demonstrate significant improvements over naive implementations via numerical evaluations on two real-world datasets (Tiny Ima-geNet and UT Zappos50K).

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Towards Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification

Neff

Pazho

Tabkhi

2023

Preprint

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Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R2OUDA) for Person Re-identification. The R2OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R2OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R2MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R2MMT was used to construct over 100 data subsets and train more than 3000 models, exploring the breadth of the R2OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world applications. R2MMT, a real-world system able to respect the strict constraints of the proposed R2OUDA setting, achieves accuracies within 0.1% of comparable OUDA methods that cannot be applied directly to real-world applications.

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Greedy $k$-Center From Noisy Distance Samples

Cited by 3 publications

References 30 publications

Real-time online unsupervised domain adaptation for real-world person re-identification

Real-time online unsupervised domain adaptation for real-world person re-identification

Greedy $k$-Center from Noisy Distance Samples

Towards Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification

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