Large-scale Training Data Search for Object Re-identification (2303.16186v1)

Abstract: We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training set from a large-scale data pool such that a competitive model can be obtained. We propose a search and pruning (SnP) solution to this training data search problem, tailored to object re-identification (re-ID), an application aiming to match the same object captured by different cameras. Specifically, the search stage identifies and merges clusters of source identities which exhibit similar distributions with the target domain. The second stage, subject to a budget, then selects identities and their images from the Stage I output, to control the size of the resulting training set for efficient training. The two steps provide us with training sets 80\% smaller than the source pool while achieving a similar or even higher re-ID accuracy. These training sets are also shown to be superior to a few existing search methods such as random sampling and greedy sampling under the same budget on training data size. If we release the budget, training sets resulting from the first stage alone allow even higher re-ID accuracy. We provide interesting discussions on the specificity of our method to the re-ID problem and particularly its role in bridging the re-ID domain gap. The code is available at https://github.com/yorkeyao/SnP.

• The paper introduces a two-stage Search and Pruning (SnP) method to select relevant training data from large pools for object re-identification, aiming to bridge the domain gap without manual annotation.
• The SnP method significantly outperforms baseline methods, achieving comparable or superior re-ID accuracy while reducing dataset size by approximately 80%.
• This approach reduces the data annotation burden for cross-domain re-ID and highlights the importance of data selection in achieving high model performance.

Large-scale Training Data Search for Object Re-identification: An Insightful Overview

The paper "Large-scale Training Data Search for Object Re-identification" introduces a novel methodology for constructing an effective training dataset tailored to the domain of object re-identification (re-ID) without the need for on-the-fly data annotation. The core idea is to leverage a large-scale data pool to derive a training set that aligns closely with the target domain’s distribution characteristics, ultimately bridging the domain gap often encountered in cross-domain re-ID applications.

Key Methodology: Search and Pruning (SnP)

The paper proposes a two-stage process, Search and Pruning (SnP), aimed at efficiently selecting a high-quality training dataset from a vast data pool:

1. Search Stage: This stage targets clustering and merging of source identities that have a distribution akin to that of the target domain. By computing feature-level distances such as Fréchet Inception Distance (FID), the authors effectively identify source clusters that minimize the domain gap with the target set. This results in a subset of the data pool that is highly relevant to the target application. Such a selection is critical because it ensures that the re-ID model is trained on data that is representative of the target conditions, thereby enhancing its domain applicability.
2. Pruning Stage: Once the suitable clusters are identified, the next step involves a refinement process constrained by a predetermined budget, typically defined by the maximum allowable size of the training dataset. This pruning process selects the most representative samples, further reducing the dataset size without compromising the model performance.

The SnP approach significantly outperforms traditional methods like random and greedy sampling, especially under constrained budget scenarios, enabling an approximately 80% reduction in data with comparable or superior re-ID accuracy.

Experimental Evaluation and Results

The proposed method demonstrates superior performance compared to benchmark methods, as shown by extensive experiments on several public re-ID datasets. Under various target conditions such as AlicePerson and AliceVehicle, the SnP method consistently results in lower FID values and higher rank-1 accuracies compared to the source pool and other baseline methods. This evidences the effectiveness of the SnP framework in constructing a domain-specific training set that enhances model generalization capabilities.

Implications and Future Directions

The primary implication of this research is its contribution to reducing the annotation burden in cross-domain re-ID tasks while still obtaining a high-performing model. The SnP method’s capability to trim down the dataset size while retaining and even enhancing model performance has practical significance in real-world applications where computational resources are limited.

Theoretically, the paper adds to the understanding of domain adaptation in deep learning by emphasizing the importance of data selection rather than merely focusing on algorithmic modifications. By demonstrating that judicious data selection can yield results on par with complex domain adaptation techniques, it opens avenues for further research into data-centric approaches in machine learning.

Future work may explore the applicability of the SnP methodology to other forms of data beyond object re-ID, such as in natural language processing or other computer vision tasks. Additionally, integrating SnP with synthetic data generation techniques could further enhance its capabilities, providing a comprehensive solution for domain adaptation across a broader spectrum of AI applications.