Zero-shot Composed Person Retrieval: Leveraging Image and Text Information for Target Person Retrieval

Temel Kavramlar

Proposing a novel task, Composed Person Retrieval (CPR), to jointly utilize visual and textual information for target person retrieval.

Özet

The content introduces the Zero-shot Composed Person Retrieval (ZS-CPR) task to address the challenges of costly manual annotations in supervised CPR. A two-stage learning framework, Word4Per, is proposed to learn the ZS-CPR model effectively. Extensive experiments demonstrate the effectiveness of Word4Per for ZS-CPR, surpassing comparative methods by over 10%.

Introduces CPR task combining visual and textual information.
Proposes ZS-CPR to resolve annotation challenges.
Presents Word4Per framework for ZS-CPR model learning.
Conducts experiments validating Word4Per's effectiveness.

İstatistikler

Conventional person retrieval methods fall short in harnessing both image and text information effectively.
ZS-CPR leverages existing domain-related data to resolve CPR problem without expensive annotations.
Word4Per framework surpasses comparative methods by over 10% in Rank-1 and mAP metrics.

Alıntılar

Önemli Bilgiler Şuradan Elde Edildi

Word4Per

by Delong Liu,H... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2311.16515.pdf

Daha Derin Sorular

How can the proposed ZS-CPR method be applied in real-world scenarios beyond research

The proposed Zero-shot Composed Person Retrieval (ZS-CPR) method has significant potential for real-world applications beyond research. One practical application could be in law enforcement and security sectors, where quick and accurate identification of individuals is crucial. ZS-CPR can be utilized in surveillance systems to track persons of interest by combining visual images with textual descriptions. This can enhance the efficiency of investigations and improve public safety.
Moreover, ZS-CPR can also find applications in retail and marketing industries. For instance, it can be used for personalized customer recommendations based on a combination of visual attributes and text descriptions provided by users. This approach could lead to more targeted advertising campaigns and improved customer engagement.
In healthcare settings, ZS-CPR could assist medical professionals in identifying patients quickly based on both visual cues from images (such as facial recognition) and relevant textual information like patient descriptions or medical history. This could streamline patient care processes and improve overall healthcare delivery.
Overall, the versatility of the ZS-CPR method makes it applicable across various industries where person retrieval tasks are essential for operational efficiency.

What are potential drawbacks or limitations of relying on existing domain-related data for ZS-CPR

While relying on existing domain-related data for Zero-shot Composed Person Retrieval (ZS-CPR) offers certain advantages such as cost-effectiveness and accessibility to readily available datasets, there are potential drawbacks and limitations that need to be considered:

Limited Diversity: Existing domain-related data may not cover all possible scenarios or variations encountered in real-world applications. This limitation could result in biased models that struggle with unseen or diverse cases during inference.

Data Quality: The quality of existing datasets may vary, leading to inconsistencies or inaccuracies that impact model performance during training and testing phases.

Domain Specificity: Utilizing domain-specific data restricts the generalizability of the model outside those specific domains. The model may not perform optimally when applied to different contexts or industries due to overfitting on limited dataset characteristics.

Privacy Concerns: Depending solely on existing datasets raises privacy concerns if sensitive information is included within the data used for training purposes without proper consent protocols.

Scalability Issues: Scaling up ZS-CPR models using only existing domain-related data might pose challenges when expanding operations into new territories or markets requiring additional annotated datasets.

How might advancements in text-image pre-training models impact the future development of CPR tasks

Advancements in text-image pre-training models have a profound impact on the future development of Composed Person Retrieval (CPR) tasks:
1- Enhanced Semantic Understanding: Improved text-image pre-training models enable better alignment between textual descriptions and visual features, enhancing semantic understanding within CPR tasks.
2- Efficient Cross-Modal Representations: Advanced pre-trained models facilitate learning rich cross-modal representations that capture intricate relationships between image content and corresponding texts.
3- Improved Generalization: State-of-the-art text-image pre-training techniques contribute towards enhanced generalization capabilities of CPR models across diverse datasets by capturing nuanced correlations between modalities.
4- Fine-grained Feature Extraction: With advancements in pre-training methods, CPR tasks benefit from fine-grained feature extraction mechanisms that capture subtle details present in both images and texts.
5-Reduced Annotation Requirements: Progression in text-image pre-training reduces dependency on extensive manual annotations by leveraging unsupervised learning approaches which further streamlines CPR model development processes.
6 -Interpretability & Explainability: Advanced text-image pre-trained models offer interpretable representations enabling better insights into how decisions are made within CPR systems improving transparency levels.