UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity (2312.03441v6)

Abstract: Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we contribute a new benchmark named \textbf{UFineBench} for text-based person retrieval with ultra-fine granularity. Firstly, we construct a new \textbf{dataset} named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions, averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation, we also propose a special \textbf{evaluation paradigm} more representative of real scenarios. It contains a new evaluation set with cross domains, cross textual granularity and cross textual styles, named UFine3C, and a new evaluation metric for accurately measuring retrieval ability, named mean Similarity Distribution (mSD). Moreover, we propose CFAM, a more efficient \textbf{algorithm} especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation, CFAM establishes competitive performance across various datasets, especially on our ultra fine-grained UFine6926. Furthermore, by evaluating on UFine3C, we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at \url{https://github.com/Zplusdragon/UFineBench}.

Analysis of UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity

The paper "UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity" presents a novel approach to address significant limitations in existing text-based person retrieval frameworks, particularly concerning the granularity of textual descriptions. The researchers introduce a new benchmark, UFineBench, which leverages fine-grained annotations to enhance retrieval tasks, enabling models to better comprehend complex query semantics reflective of real-world applications.

Overview

The authors identify a gap in current datasets, which often exhibit coarse-grained annotations, typically resulting in algorithmic degradation into attribute-based retrieval. To resolve this, they present UFine6926, a dataset containing 6,926 identities with extensive textual descriptions, averaging 80.8 words per image, significantly extending the descriptive detail compared to previous works. The dataset draws images from diverse, unconstrained sources and incorporates meticulous manual annotation to ensure high-quality, detailed text-to-image mappings.

Furthermore, the paper introduces UFine3C, an evaluation set designed to more accurately reflect real-world conditions via cross-domain, cross-textual granularity, and cross-textual styles, better preparing models for the variability found in practice. A novel metric, mean Similarity Distribution (mSD), is proposed to address deficiencies in existing evaluation methods that rely on discrete rank measures, thus offering a more nuanced analysis of retrieval performance by leveraging continuous similarity distributions.

Methodology

The paper advances a new framework, the Cross-modal Fine-grained Aligning and Matching (CFAM), which capitalizes on shared cross-modal granularity decoders and a hard negative match mechanism to achieve superior model performance. The CFAM framework demonstrates strong retrieval capabilities across multiple datasets by enhancing both local and global alignment of visual and textual data through meticulously designed interaction and learning strategies.

Empirical Evaluation

The evaluations presented showcase CFAM's competitive performance across both in-domain and cross-domain scenarios, with particular emphasis on the associated gains derived from the newly introduced UFine6926 dataset. Notably, CFAM's adaptability is underscored through its robust generalization across diverse datasets, signifying its potential utility in real-world settings characterized by significant variability and noise.

Implications and Future Directions

This research not only sets a foundation for improved text-based person retrieval through fine-grained descriptors but also opens new avenues for AI applications that demand high precision in understanding human-centric query semantics. The introduction of the UFineBench framework and associated methodologies highlights the nuanced interplay required between sophisticated data annotation and algorithmic innovation.

Moving forward, the insights gleaned from this research could spur further advancements in the development of multimodal frameworks, particularly those that seek to leverage ultra-fine granularity in contexts such as surveillance, personalized recommender systems, and human-computer interaction. Future investigations might explore integration with larger, more diverse data sets, or the incorporation of advanced neural network architectures to further optimize retrieval accuracy and computational efficiency.

In sum, the contributions of this paper enrich the discourse on text-based person retrieval by advocating for a paradigm shift towards granularity, precision, and contextual understanding, thereby advancing the theoretical and practical utility of AI in this domain.