TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search (2105.11628v1)

Abstract: Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this problem in the fine-grained level. However, most relevant methods introduce additional models or complicated training and evaluation strategies, which are hard to use in realistic scenarios. In order to facilitate the practical application, we propose a simple but effective end-to-end learning framework for text-based person search named TIPCB (i.e., Text-Image Part-based Convolutional Baseline). Firstly, a novel dual-path local alignment network structure is proposed to extract visual and textual local representations, in which images are segmented horizontally and texts are aligned adaptively. Then, we propose a multi-stage cross-modal matching strategy, which eliminates the modality gap from three feature levels, including low level, local level and global level. Extensive experiments are conducted on the widely-used benchmark dataset (CUHK-PEDES) and verify that our method outperforms the state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.

An Examination of TIPCB for Text-Based Person Search

The task of text-based person search, a key challenge in the domain of cross-modal image retrieval, requires methods that efficiently bridge the significant feature gap between text and image modalities. Traditional approaches often rely on complex alignment and additional models, which complicate practical implementation. Addressing this, the paper proposes the Text-Image Part-based Convolutional Baseline (TIPCB), an end-to-end framework designed to simplify and enhance the task of text-based person search.

TIPCB introduces a novel dual-path local alignment network that comparatively evaluates the visual and textual local representations. At the core of the visual representation process is the use of a ResNet-50 backbone paired with the Part-based Convolutional Baseline (PCB) strategy, which segments images horizontally into local stripes. This strategy aims to maintain detailed discriminative elements that global representations may overlook. On the textual side, the use of a pre-trained BERT model extracts word embeddings, which are then processed through a multi-branch residual network to ensure adaptive alignment with visual features.

One of the standout methodologies in TIPCB is the multi-stage cross-modal matching strategy. This novel approach seeks to address the feature gap progressively across different levels—low, local, and global. By applying the Cross-Modal Projection Matching (CMPM) loss at each level, the model iteratively improves the compatibility of visual and textual features, demonstrating marked improvement over single-level approaches.

The paper reports significant empirical outcomes, notably on the CUHK-PEDES dataset, where TIPCB surpasses state-of-the-art performance by 3.69%, 2.95%, and 2.31% at Top-1, Top-5, and Top-10 ranks, respectively. Such results underline the efficacy of the dual-path and multi-level alignment strategies in enhancing the discrimination power of the model across modalities.

The implications of these results extend into practical realms, notably in scenarios such as public security and surveillance where quick and accurate person retrieval is pivotal. Furthermore, the simplicity and effectiveness of TIPCB open pathways for future research into end-to-end solutions for cross-modal tasks, potentially reducing reliance on additional computational models.

Future developments in AI could capitalize on the efficient design principles showcased in TIPCB, fostering advancements in real-time, cross-modal person search systems. This paper lays down a substantial foundation for subsequent research, providing a benchmark in achieving effective cross-modal retrieval within streamlined frameworks. Thus, TIPCB not only enriches the theoretical discourse on multi-modal learning but also presents a practical solution adaptable to various cross-domain applications.