PLIP: Language-Image Pre-training for Person Representation Learning

This figure illustrates the PLIP framework, which incorporates three pretext tasks: text-guided image colorization (TIC), image-guided attributes prediction (IAP), and identity-based vision-language contrast (IVLC). The framework uses a dual-branch encoder structure (visual and textual encoders) to learn generic person representations. TIC aims to restore color to a grayscale person image using textual descriptions, helping the model learn relationships between image regions and textual phrases. IAP predicts masked attribute phrases in a description using color images, enabling understanding of key areas and semantic concepts. IVLC associates representations at the identity level, rather than the instance level, which is crucial for distinguishing different people. The whole process uses a combined loss to train the model, explicitly learning fine-grained and meaningful cross-modal associations.

This figure shows three sets of examples from the SYNTH-PEDES dataset. Each set contains three images of the same person with three different captions describing the person’s attributes and attire. The captions demonstrate the variety and detail of the automatically generated descriptions in SYNTH-PEDES.

This figure illustrates the overall architecture of the PLIP framework. It shows three main pretext tasks: Text-guided Image Colorization (TIC), Image-guided Attributes Prediction (IAP), and Identity-based Vision-Language Contrast (IVLC). The visual and textual encoders are shown, along with the specific processing steps for each task. TIC aims to restore color to a grayscale image guided by text. IAP predicts masked attribute phrases in a textual description using the corresponding image. IVLC links visual and textual representations at the identity level, rather than instance level. The figure demonstrates the dual-branch encoder structure and how the three tasks work together to learn generic and discriminative person representations.

This figure shows the results of a text-guided image colorization task. Multiple rows display the same grayscale person image, but each column represents a different colorization result based on changing a single color word in the corresponding textual description. This demonstrates how the model interprets and applies color information from the text to different image regions.

This figure illustrates the overall framework of the proposed PLIP model. The framework begins with constructing a large-scale dataset of image-text pairs. This dataset is then used to pre-train a language-image model using three different pretext tasks: Text-guided Image Colorization, Image-guided Attributes Prediction, and Identity-based Vision-Language Contrast. Finally, the pre-trained model is transferred to various downstream person-centric tasks such as image-based and text-based re-identification, attribute recognition, person search, and human parsing.

This figure illustrates the overall framework of the proposed PLIP model. It consists of three stages: 1) a large-scale dataset construction stage, where a large-scale person dataset with image-text pairs is created; 2) a language-image pre-training stage, where the language-image model is pre-trained using three pretext tasks; and 3) a downstream task transfer stage, where the pre-trained model is transferred to several person-centric downstream tasks for evaluation. The three pretext tasks are: Text-guided Image Colorization, Image-guided Attributes Prediction, and Identity-based Vision-Language Contrast.

This figure shows the overall architecture of the PLIP framework, which incorporates three pretext tasks: text-guided image colorization (TIC), image-guided attributes prediction (IAP), and identity-based vision-language contrast (IVLC). The visual and textual encoders process the input image and text respectively. The three pretext tasks work together to learn fine-grained and meaningful cross-modal associations for person representation learning. Each task’s processing steps are shown within the framework diagram.

This figure illustrates the PLIP framework, a novel language-image pre-training framework for person representation learning. It shows the three pretext tasks used: Text-guided Image Colorization (TIC), Image-guided Attributes Prediction (IAP), and Identity-based Vision-Language Contrast (IVLC). The framework uses a dual-branch encoder structure (visual and textual encoders) to learn generic person representations by jointly training on these three pretext tasks. The figure also highlights the process of feature extraction, fusion, and prediction for each task.

This figure illustrates the PLIP framework, showing the three pretext tasks used for pre-training: Text-guided Image Colorization (TIC), Image-guided Attributes Prediction (IAP), and Identity-based Vision-Language Contrast (IVLC). It shows the dual-branch encoder structure (visual and textual) and how the three tasks are integrated. The figure highlights the flow of information through the encoders and the fusion of visual and textual features for each task. The overall architecture is designed to learn fine-grained and meaningful cross-modal associations for person representation learning.

The figure provides a visual representation of the PLIP framework, highlighting its three main pretext tasks: text-guided image colorization (TIC), image-guided attribute prediction (IAP), and identity-based vision-language contrast (IVLC). It shows the dual-branch encoder structure of the model, how each pretext task works, and the flow of information between the visual and textual encoders. TIC focuses on restoring color information to grayscale images using textual descriptions. IAP focuses on predicting masked attribute words in the description based on the image. IVLC focuses on contrasting representations at the identity level instead of the instance level. The figure illustrates the overall architecture and the interconnections of the three pretext tasks.

The figure illustrates the PLIP framework, which incorporates three pretext tasks: text-guided image colorization (TIC), image-guided attributes prediction (IAP), and identity-based vision-language contrast (IVLC). TIC aims to restore color information in a grayscale image guided by text. IAP predicts masked attribute phrases in text descriptions given the corresponding image. IVLC aligns visual and textual representations at the identity level to learn more meaningful cross-modal associations. The framework consists of dual-branch encoders (visual and textual) and three pretext task modules. The output of the encoders, representing visual and textual features, is fused for cross-modal learning and downstream tasks.

This table presents the results of transfer learning experiments for text-based person re-identification using different settings (zero-shot, linear probing, and fine-tuning). The table compares the performance of the proposed model (PLIP) against state-of-the-art methods (VITAA, SSAN, LapsCore, TIPCB, LGUR) on two benchmark datasets (CUHK-PEDES and ICFG-PEDES). The performance metrics used are Recall@1, Recall@5, and Recall@10.

This table presents the results of a domain generalization experiment in text-based person re-identification. Two datasets, CUHK-PEDES (C) and ICFG-PEDES (I), were used. The experiment evaluates the performance of different models when transferring knowledge from one dataset to the other. The results show the Rank-1, Rank-5, and Rank-10 accuracy rates for each model in both cross-domain settings (C→I and I→C). The table highlights the performance of the proposed PLIP model compared to several existing state-of-the-art models.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SoTA) pre-trained models on five downstream person-centric tasks: image-based Re-ID, text-based Re-ID, attribute recognition, person search, and human parsing. The table is divided into two sections: the first showing general-domain pre-trained models and the second showing person-domain pre-trained models. Baseline methods for each task are also included, providing context for comparing the performance improvements achieved by using different pre-trained models. Note that some models’ results are marked with a ‘-’ due to incompatibility with certain downstream methods.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SOTA) methods on the task of text-based person re-identification. It evaluates the models on two datasets: CUHK-PEDES and ICFG-PEDES. The table shows the results for various performance metrics including rank-1, rank-5, and rank-10 accuracy. The results highlight the superior performance of PLIP compared to existing methods.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SoTA) methods on two widely used datasets for image-based person re-identification: Market1501 and DukeMTMC. The results showcase the improvements achieved by PLIP on both datasets. Note that results from post-processing techniques (like re-ranking) are excluded to maintain a fair comparison.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SoTA) methods on two benchmark datasets for image-based person re-identification: Market1501 and DukeMTMC. The table shows the mAP (mean Average Precision) and Rank-1 accuracy for each method, broken down by backbone architecture (e.g., ResNet50, ViT-B, Swin-B) and pre-training method (IMG, LUP, LUP-NL, SYNTH). The results demonstrate that PLIP, particularly with Swin-B backbone and SYNTH pre-training, significantly outperforms SoTA methods.

This table compares the performance of the proposed PLIP model with other state-of-the-art (SoTA) methods on text-based person re-identification. The comparison includes both methods that rely on multi-modal pre-trained models and methods that do not. The results are presented for different metrics (R@1, R@5, R@10) on multiple datasets (CUHK-PEDES and ICFG-PEDES). The table highlights that PLIP consistently outperforms other methods, especially those not using multi-modal pre-trained models.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SoTA) methods on two person search benchmark datasets: CUHK-SYSU and PRW. The results are shown for two metrics: mean Average Precision (mAP) and Rank@1. PLIP consistently outperforms prior SoTA methods, demonstrating its effectiveness for person search tasks.

This ablation study investigates the individual and combined effects of the three pretext tasks (IVLC, TIC, and IAP) on the performance of the PLIP model. It shows the zero-shot performance (R@1, R@5, R@10) on the CUHK-PEDES and Market1501 datasets for different combinations of the pretext tasks, demonstrating their individual and combined contributions to the overall model performance.

This table compares the performance of the proposed PLIP model with other state-of-the-art (SoTA) pre-trained models on five downstream person-centric tasks: image-based Re-ID, text-based Re-ID, attribute recognition, person search, and human parsing. It’s divided into two sections: general-domain pre-trained models and person-domain pre-trained models. The baseline methods used for each task are also specified. Some models are not applicable to all tasks due to their hierarchical or non-hierarchical nature, indicated by a ‘-’ in the table.

This table compares the statistics of several existing person re-identification datasets, including the number of images, identities, descriptions, camera type, label type, labeling method, and crop size. It highlights that the proposed SYNTH-PEDES dataset is significantly larger than other datasets, containing a much greater number of images, identities, and textual descriptions, while not requiring manual annotation.

This table compares the performance of the proposed PLIP model with other state-of-the-art (SoTA) pre-trained models on five downstream person-centric tasks: image-based Re-ID, text-based Re-ID, attribute recognition, person search, and human parsing. The table is divided into two sections: general-domain pre-trained models and person-domain pre-trained models. For each task, baseline methods are listed, and the performance (e.g., mAP, Rank-1) of each pre-trained model is shown. Some baselines are not applicable to all models, resulting in ‘-’ entries.

This table presents the ablation study results on the impact of each pretext task (IVLC, TIC, IAP) in the proposed PLIP framework. It shows the performance (R@1, R@5, R@10) on two person re-identification benchmarks, CUHK-PEDES and Market1501, under different combinations of the pretext tasks. Row 1 shows results with only IVLC; Row 2 adds TIC; Row 3 adds IAP. Row 4 shows only using TIC and IAP, and Row 5 uses all three pretext tasks. This demonstrates the contribution of each component to the overall performance.

This table presents the results of a manual evaluation comparing the quality of the SYNTH-PEDES dataset to three other manually annotated datasets (CUHK-PEDES, ICFG-PEDES, RSTPReid) and one synthetic dataset (FineGPR-C). The evaluation used a five-point scale, assessing aspects like correctness and detail in the textual descriptions of images. SYNTH-PEDES demonstrates comparable quality to the manually annotated datasets, despite its significantly larger size, and outperforms the synthetic dataset.

This ablation study analyzes the impact of different choices in the training process of the PLIP model. Specifically, it investigates the effect of different training objectives for the IVLC and TIC pretext tasks, explores the influence of varying prediction difficulty levels (controlled by a function G(x)), and examines the impact of different pooling methods for combining visual and textual features. The results show the relative importance of each component and help to optimize the model’s performance.

This table presents the results of experiments conducted to find the optimal hyperparameters for the overall objective function used in the PLIP model. The objective function is a combination of three loss functions (IVLC, TIC, and IAP), and the table shows the performance (R@1, R@5, R@10) on the CUHK-PEDES and Market1501 datasets for various combinations of the hyperparameters λ₁ and λ₂. The results demonstrate the impact of different weighting schemes on the overall performance of the model, indicating that a particular combination yields the best performance.

This table compares the performance of the PLIP pre-trained model against other state-of-the-art (SoTA) pre-trained models on five downstream person-centric tasks: image-based re-identification, text-based re-identification, attribute recognition, person search, and human parsing. The table is divided into two sections: the first compares general-domain pre-trained models, and the second compares person-domain pre-trained models. Baseline methods for each task are listed, and the performance (metrics vary by task) of each model using different backbones (e.g., ResNet50, ResNet101, etc.) is shown. Dashes indicate when a model could not be applied to a particular task.

This table presents the results of domain generalization experiments for text-based person re-identification. The model’s performance is evaluated on two datasets, CUHK-PEDES (C) and ICFG-PEDES (I), with each model tested on both datasets as the target domain, given a source dataset for training. This assesses the model’s ability to generalize to unseen domains. The metrics used to evaluate performance are R@1, R@5, and R@10.

This table compares the performance of different pre-trained models (MoCov2, CLIP, LUP, LUP-NL, and PLIP) when used with two baseline person attribute recognition methods (Rethink and DeepMAR). It shows the improvement in mean accuracy (mA), accuracy (Acc), recall (Rec), and F1-score for both the PA100k and PETA datasets. The results demonstrate that the PLIP pre-trained model significantly improves the performance of both baseline methods.

This table compares the performance of the proposed PLIP model against other state-of-the-art (SoTA) methods on the task of human parsing. Two datasets are used for evaluation: LIP and PASCAL-Person-Part. The table shows the mean Intersection over Union (mIoU) metric, a common evaluation metric for semantic segmentation tasks like human parsing. Different backbone networks are used for the models. The results demonstrate that PLIP achieves competitive or superior performance compared to SoTA methods.