Abstract

Coreset Selection (CS) aims to extract a small yet representative subset from a large dataset, reducing the complexity of model training. Although CS has been primarily investigated for classification tasks, it is still underexplored for object Re-identification (ReID). In this paper, we first formulate Coreset Selection for Object Re-identification (CSOR) as a joint optimization problem to find both the optimal coreset and the optimal class subset. We identify intra-class diversity as a key factor for effective coreset construction for ReID. Based on this insight, we propose a novel two-stage framework, consisting of Diversity-driven Class Pruning (DCP) and Coverage-Prioritized Sampling (CPS), to address the unique challenges of ReID datasets. First, classes with low feature diversity are pruned to allocate the storage budget to the remaining informative classes. Then, samples are greedily selected in an easy-to-hard class order to maximize feature coverage within each class. Extensive experiments on three person ReID datasets and one vehicle ReID dataset demonstrate that our method consistently outperforms existing CS approaches, establishing a new state-of-the-art in CSOR.

Video datasets used to train re-identification systems often contain near-duplicate images. The same person or vehicle may appear across many consecutive frames with little change, increasing storage and computing demands without adding equally useful information.

Led by Professor Jae-Young Sim in the UNIST Graduate School of Artificial Intelligence, the research team has developed a method that filters out much of this redundancy, while preserving the visual diversity needed for training. The method, called Coreset Selection for Object Re-identification (CSOR), selects a compact set of representative images from a much larger dataset.

“Re-identification models must distinguish entirely new people or vehicles that were not included in the training data,” said first author Minyoung Oh, a researcher at UNIST. “We began with this distinction and redesigned the selection criteria around the specific demands of ReID.”

The researchers formulated CSOR as a joint optimization problem that determines both which identity classes and which images to retain. Their analysis identified diversity within each class—the range of appearances captured for the same person or vehicle—as a key factor in building an effective training subset. 

CSOR follows a two-stage process. Diversity-driven Class Pruning (DCP) first removes identity classes with little feature variation, preserving more of the storage budget for classes with richer information. Coverage-Prioritized Sampling (CPS) then selects images from the remaining classes, giving priority to those that capture the broader range of features.

The team evaluated CSOR on three person and one vehicle ReID datasets. The method consistently outperformed the CS approaches used for comparison. Models trained on approximately half of the original data retained more than 95% of the performance achieved using the full dataset.

“ReID datasets are commonly built from video, so duplicate images accumulate quickly as the same person or vehicle appears across consecutive frames,” said Professor Sim. “CSOR removes much of this repetition while retaining the diversity needed for ReID. It could make training and deployment more practical on edge devices with limited storage and computing resources.”

Their findings were accepted for presentation at the 2026 International Conference on Machine Learning (ICML), held at COEX in Seoul in July 2026. The study was supported by the National Research Foundation of Korea (NRF) and the Institute for Information & Communications Technology Planning & Evaluation (IITP), with funding from the Ministry of Science and ICT (MSIT). Support was provided through the Mid-Career Researcher Program, the AI Graduate School Program, the AI Star Fellowship Program, and an initiative for training in industrially integrated multimodal generative AI.