Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, Peng Wang·June 18, 2024

Summary

The paper presents a novel approach, Modality-aware and Instance-aware Visual Prompts (MIP), for enhancing Visible-Infrared Person Re-identification (VI ReID). MIP employs a transformer-based network that integrates modality-specific and instance-specific prompts to address the modality gap and capture discriminative cues. The Modality-aware Prompt Learning (MPL) module adapts to different modalities, while the Instance-aware Prompt Generator (IPG) creates instance-specific prompts for better person representation. Experiments on SYSU-MM01 and RegDB datasets demonstrate MIP's superior performance over state-of-the-art methods, highlighting its effectiveness in leveraging modality-specific and instance-level information for improved re-identification accuracy. The study also suggests that combining modality-specific and instance-aware components is crucial for enhancing cross-modal person recognition.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of adapting models to different modality inputs in the context of person re-identification by utilizing Modality-aware and Instance-aware Visual Prompts (MIP) network . This problem is not entirely new, as existing methods have focused on eliminating modality-specific information without fully utilizing its potential, overlooking the benefits that such information could provide for re-identification . The paper proposes a novel approach that leverages both modality-invariant and modality-specific information to enhance identification, recognizing the importance of utilizing both types of information effectively .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Visible-Infrared Person Re-identification (VI ReID). The hypothesis focuses on matching visible and infrared images of the same individuals across different camera views, utilizing both invariant information like shape and modality-specific details such as color to enhance the re-identification process . The study explores methods to bridge the domain gaps between visible and infrared modalities to improve the versatility and applicability of ReID techniques across various environmental settings .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the field of Visible-Infrared Person Re-identification (VI ReID) through the following key contributions:

  1. Modality- and Instance-aware Visual Prompt Learning: The paper introduces a novel approach that focuses on enhancing VI ReID by incorporating modality- and instance-aware visual prompt learning . This method involves generating instance-specific prompts to adapt to diverse instances effectively. By utilizing generation-based IPG modules, the proposed approach significantly improves performance compared to fusion-based IPG modules .

  2. Specific Prompt Design for Modality Inputs and Instances: The paper suggests designing specific prompts for different modality inputs and instances to address the challenges in VI ReID . This tailored prompt strategy aims to extract discriminative cross-modality invariant features at the part level, enhancing the overall performance of VI ReID systems .

  3. Comparison with State-of-the-art Methods: The study compares the proposed Modality- and Instance-aware Visual Prompt Learning (MIP) method with existing state-of-the-art techniques in VI ReID, showcasing superior performance across mainstream datasets like SYSU-MM01 and RegDB . The MIP method outperforms transformer-based methods like PMT, CMTR, DFLN-ViT, SPOT, and CNN-based methods like TOPLight, demonstrating advancements in VI ReID accuracy .

  4. Incorporation of Visual Prompt Learning in VI ReID: The paper explores the application of visual prompt learning in the VI ReID domain, drawing inspiration from successful implementations in other computer vision tasks . By leveraging specific prompts for different modalities and instances, the proposed approach aims to adapt original models effectively to VI ReID tasks, filling a gap in the current research landscape .

Overall, the paper's contributions lie in the development of a sophisticated Modality- and Instance-aware Visual Prompt Learning approach tailored for VI ReID, emphasizing the importance of prompt design, instance-specific adaptation, and performance benchmarking against existing methodologies in the field. The paper introduces novel characteristics and advantages compared to previous methods in Visible-Infrared Person Re-identification (VI ReID) through the following key points:

  1. Modality- and Instance-aware Visual Prompt Learning: The paper proposes a unique approach that focuses on enhancing VI ReID by incorporating modality- and instance-aware visual prompt learning . This method involves generating instance-specific prompts to adapt to diverse instances effectively, leading to improved performance compared to fusion-based IPG modules .

  2. Tailored Prompt Design for Modality Inputs and Instances: The study suggests designing specific prompts for different modality inputs and instances to address challenges in VI ReID . By tailoring prompts to extract discriminative cross-modality invariant features at the part level, the proposed approach enhances VI ReID systems' overall performance .

  3. Comparison with State-of-the-art Methods: The research compares the proposed Modality- and Instance-aware Visual Prompt Learning (MIP) method with existing state-of-the-art techniques in VI ReID, showcasing superior performance across mainstream datasets like SYSU-MM01 and RegDB . The MIP method outperforms transformer-based methods like PMT, CMTR, DFLN-ViT, SPOT, and CNN-based methods like TOPLight, demonstrating advancements in VI ReID accuracy .

  4. Incorporation of Visual Prompt Learning in VI ReID: The paper explores the application of visual prompt learning in the VI ReID domain, drawing inspiration from successful implementations in other computer vision tasks . By leveraging specific prompts for different modalities and instances, the proposed approach aims to adapt original models effectively to VI ReID tasks, filling a gap in the current research landscape .

Overall, the paper's contributions lie in the development of a sophisticated Modality- and Instance-aware Visual Prompt Learning approach tailored for VI ReID, emphasizing the importance of prompt design, instance-specific adaptation, and performance benchmarking against existing methodologies in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of Visible-Infrared Person Re-identification (VI ReID) as highlighted in the provided document. Noteworthy researchers in this field include Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, and Peng Wang . These researchers have contributed to the development of solutions for VI ReID through their work on enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning.

The key to the solution mentioned in the paper involves utilizing modality-specific details to reveal potential relationships in Visible-Infrared Person Re-identification. The approach integrates modality-specific attributes like color, texture, and brightness to extract and explore potential relationships, which is crucial for addressing modality discrepancies in VI ReID tasks . Additionally, the paper discusses the use of visual prompt learning, inspired by textual prompts in NLP, to adapt original models to other tasks effectively, showcasing the potential of this approach in VI ReID .


How were the experiments in the paper designed?

The experiments in the paper were designed through a series of ablation studies and comparisons to evaluate the effectiveness of the proposed components and modules . The experiments started by validating the effectiveness of each component, followed by comparing the designed MPL and IPG modules with general prompt-based approaches . Additionally, the necessity of the generation-based design of the IPG module was discussed, and the effect of the MPL module on extracting implicit correspondence between modality-specific information was presented . These experiments were primarily conducted on the SYSU-MM01 dataset under the Single-Shot mode, unless specified otherwise .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is RegDB, which consists of 412 persons with visible and infrared images captured by respective cameras . The evaluation metrics include the Cumulative Matching Characteristic curve (CMC) and mean Average Precision (mAP) . The code for the proposed MIP method is implemented using the Pytorch framework . However, it is not explicitly mentioned in the provided context whether the code is open source or publicly available.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces a Modality-aware and Instance-aware Visual Prompts (MIP) network, which includes a Modality-aware Prompt Learning (MPL) module and an Instance-aware Prompt Generator (IPG) module . These components are designed to enhance person re-identification by leveraging modality-specific and instance-specific prompts to guide the model's adaptation and feature extraction process .

The experiments conducted in the paper include ablation studies to evaluate the effectiveness of the proposed components, such as the MPL module, IPG module, and IAEL loss . The results demonstrate the incremental performance improvements achieved by gradually adding these components to the baseline model, highlighting the importance of each component in enhancing the model's capabilities .

Furthermore, the paper compares the designed MPL and IPG modules with general prompt-based approaches, showcasing the superiority of the proposed approach in extracting implicit correspondence between modality-specific information and guiding the model's adaptation dynamically . This comparative analysis provides valuable insights into the effectiveness of the novel prompt learning strategy employed in the MIP network.

Overall, the experimental results presented in the paper offer compelling evidence to support the scientific hypotheses underlying the development and implementation of the Modality-aware and Instance-aware Visual Prompts (MIP) network for visible-infrared person re-identification . The thorough analysis and comparisons conducted in the experiments validate the efficacy of the proposed components and demonstrate their significant contributions to improving the model's performance in challenging re-identification tasks.


What are the contributions of this paper?

The contributions of the paper "Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning" include:

  • Introducing Modality- and Instance-aware Visual Prompt Learning: The paper introduces a novel approach that leverages modality- and instance-aware visual prompt learning to enhance visible-infrared person re-identification .
  • Improving Person Re-identification: The research aims to improve the matching of visible and infrared images of the same individuals across different camera views, focusing on utilizing both invariant information like shape and modality-specific details such as color for enhanced re-identification .
  • Addressing Modality-Specific Challenges: The study addresses challenges related to modality-specific features and details by incorporating modality-aware learning strategies into the re-identification process .
  • Enhancing Training with Valuable Information: The paper emphasizes the importance of utilizing valuable information from both visible and infrared modalities during training to enhance the person re-identification process .

What work can be continued in depth?

Further research in the field of Visible-Infrared Person Re-identification can be expanded in several directions based on the existing works:

  • Exploring Modality-Specific Features: Future studies can delve deeper into understanding and utilizing modality-specific features for person re-identification tasks, as highlighted by previous research .
  • Enhancing Visual Prompt Learning: There is potential to further optimize and refine visual prompt learning techniques for adapting models to various tasks, as demonstrated in computer vision tasks .
  • Investigating Cross-Modality Consistency: Research can focus on exploring and leveraging cross-modality consistency information to improve the performance of person re-identification models across different modalities .
  • Instance-Specific Adaptation: Future work can concentrate on developing more sophisticated methods for instance-specific adaptation in person re-identification models to capture discriminative clues for identification more effectively .
  • Utilizing Transformer Architectures: Given the success of transformer architectures in various tasks, there is a scope to further investigate the application of transformer-based models in Visible-Infrared Person Re-identification for improved performance .
  • Addressing Modality Discrepancies: Researchers can focus on developing innovative approaches to handle modality discrepancies effectively, such as feature alignment and fusion operations, to enhance the accuracy of person re-identification models .

Tables

5

Introduction
Background
Overview of Visible-Infrared Person ReID (VI ReID) challenges
Importance of addressing modality gap in cross-modal recognition
Objective
To propose MIP: a novel approach for VI ReID
Aim to improve re-identification accuracy by leveraging modality-specific and instance-level cues
Method
Modality-aware Prompt Learning (MPL) Module
Architecture
Transformer-based network design
Modality Adaptation
Integration of modality-specific features
Handling differences between visible and infrared modalities
Training Strategy
Adapting prompts to different modalities
Instance-aware Prompt Generator (IPG)
Functionality
Creation of instance-specific prompts for enhanced person representation
Generation Process
Capturing discriminative cues at the instance level
Integration with MPL
Collaborative learning of modality-aware and instance-aware prompts
Experiments
Datasets
SYSU-MM01 dataset
RegDB dataset
Evaluation
Performance comparison with state-of-the-art methods
Analysis of re-identification accuracy improvements
Findings
MIP's superior performance
Importance of combining modality-specific and instance-aware components
Conclusion
Summary of MIP's effectiveness in VI ReID
Implications for future cross-modal person recognition research
Limitations and potential directions for improvement
Basic info
papers
computer vision and pattern recognition
multimedia
artificial intelligence
Advanced features
Insights
How does the Modality-aware and Instance-aware Visual Prompts (MIP) address the modality gap in VI ReID?
What is the key takeaway about the combination of modality-specific and instance-aware components in enhancing cross-modal person recognition?
Which datasets are used for evaluating the performance of MIP compared to other state-of-the-art methods?
What is the primary focus of the paper regarding person re-identification?

Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, Peng Wang·June 18, 2024

Summary

The paper presents a novel approach, Modality-aware and Instance-aware Visual Prompts (MIP), for enhancing Visible-Infrared Person Re-identification (VI ReID). MIP employs a transformer-based network that integrates modality-specific and instance-specific prompts to address the modality gap and capture discriminative cues. The Modality-aware Prompt Learning (MPL) module adapts to different modalities, while the Instance-aware Prompt Generator (IPG) creates instance-specific prompts for better person representation. Experiments on SYSU-MM01 and RegDB datasets demonstrate MIP's superior performance over state-of-the-art methods, highlighting its effectiveness in leveraging modality-specific and instance-level information for improved re-identification accuracy. The study also suggests that combining modality-specific and instance-aware components is crucial for enhancing cross-modal person recognition.
Mind map
Collaborative learning of modality-aware and instance-aware prompts
Capturing discriminative cues at the instance level
Creation of instance-specific prompts for enhanced person representation
Adapting prompts to different modalities
Handling differences between visible and infrared modalities
Integration of modality-specific features
Transformer-based network design
Importance of combining modality-specific and instance-aware components
MIP's superior performance
Analysis of re-identification accuracy improvements
Performance comparison with state-of-the-art methods
RegDB dataset
SYSU-MM01 dataset
Integration with MPL
Generation Process
Functionality
Training Strategy
Modality Adaptation
Architecture
Aim to improve re-identification accuracy by leveraging modality-specific and instance-level cues
To propose MIP: a novel approach for VI ReID
Importance of addressing modality gap in cross-modal recognition
Overview of Visible-Infrared Person ReID (VI ReID) challenges
Limitations and potential directions for improvement
Implications for future cross-modal person recognition research
Summary of MIP's effectiveness in VI ReID
Findings
Evaluation
Datasets
Instance-aware Prompt Generator (IPG)
Modality-aware Prompt Learning (MPL) Module
Objective
Background
Conclusion
Experiments
Method
Introduction
Outline
Introduction
Background
Overview of Visible-Infrared Person ReID (VI ReID) challenges
Importance of addressing modality gap in cross-modal recognition
Objective
To propose MIP: a novel approach for VI ReID
Aim to improve re-identification accuracy by leveraging modality-specific and instance-level cues
Method
Modality-aware Prompt Learning (MPL) Module
Architecture
Transformer-based network design
Modality Adaptation
Integration of modality-specific features
Handling differences between visible and infrared modalities
Training Strategy
Adapting prompts to different modalities
Instance-aware Prompt Generator (IPG)
Functionality
Creation of instance-specific prompts for enhanced person representation
Generation Process
Capturing discriminative cues at the instance level
Integration with MPL
Collaborative learning of modality-aware and instance-aware prompts
Experiments
Datasets
SYSU-MM01 dataset
RegDB dataset
Evaluation
Performance comparison with state-of-the-art methods
Analysis of re-identification accuracy improvements
Findings
MIP's superior performance
Importance of combining modality-specific and instance-aware components
Conclusion
Summary of MIP's effectiveness in VI ReID
Implications for future cross-modal person recognition research
Limitations and potential directions for improvement
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of adapting models to different modality inputs in the context of person re-identification by utilizing Modality-aware and Instance-aware Visual Prompts (MIP) network . This problem is not entirely new, as existing methods have focused on eliminating modality-specific information without fully utilizing its potential, overlooking the benefits that such information could provide for re-identification . The paper proposes a novel approach that leverages both modality-invariant and modality-specific information to enhance identification, recognizing the importance of utilizing both types of information effectively .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Visible-Infrared Person Re-identification (VI ReID). The hypothesis focuses on matching visible and infrared images of the same individuals across different camera views, utilizing both invariant information like shape and modality-specific details such as color to enhance the re-identification process . The study explores methods to bridge the domain gaps between visible and infrared modalities to improve the versatility and applicability of ReID techniques across various environmental settings .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the field of Visible-Infrared Person Re-identification (VI ReID) through the following key contributions:

  1. Modality- and Instance-aware Visual Prompt Learning: The paper introduces a novel approach that focuses on enhancing VI ReID by incorporating modality- and instance-aware visual prompt learning . This method involves generating instance-specific prompts to adapt to diverse instances effectively. By utilizing generation-based IPG modules, the proposed approach significantly improves performance compared to fusion-based IPG modules .

  2. Specific Prompt Design for Modality Inputs and Instances: The paper suggests designing specific prompts for different modality inputs and instances to address the challenges in VI ReID . This tailored prompt strategy aims to extract discriminative cross-modality invariant features at the part level, enhancing the overall performance of VI ReID systems .

  3. Comparison with State-of-the-art Methods: The study compares the proposed Modality- and Instance-aware Visual Prompt Learning (MIP) method with existing state-of-the-art techniques in VI ReID, showcasing superior performance across mainstream datasets like SYSU-MM01 and RegDB . The MIP method outperforms transformer-based methods like PMT, CMTR, DFLN-ViT, SPOT, and CNN-based methods like TOPLight, demonstrating advancements in VI ReID accuracy .

  4. Incorporation of Visual Prompt Learning in VI ReID: The paper explores the application of visual prompt learning in the VI ReID domain, drawing inspiration from successful implementations in other computer vision tasks . By leveraging specific prompts for different modalities and instances, the proposed approach aims to adapt original models effectively to VI ReID tasks, filling a gap in the current research landscape .

Overall, the paper's contributions lie in the development of a sophisticated Modality- and Instance-aware Visual Prompt Learning approach tailored for VI ReID, emphasizing the importance of prompt design, instance-specific adaptation, and performance benchmarking against existing methodologies in the field. The paper introduces novel characteristics and advantages compared to previous methods in Visible-Infrared Person Re-identification (VI ReID) through the following key points:

  1. Modality- and Instance-aware Visual Prompt Learning: The paper proposes a unique approach that focuses on enhancing VI ReID by incorporating modality- and instance-aware visual prompt learning . This method involves generating instance-specific prompts to adapt to diverse instances effectively, leading to improved performance compared to fusion-based IPG modules .

  2. Tailored Prompt Design for Modality Inputs and Instances: The study suggests designing specific prompts for different modality inputs and instances to address challenges in VI ReID . By tailoring prompts to extract discriminative cross-modality invariant features at the part level, the proposed approach enhances VI ReID systems' overall performance .

  3. Comparison with State-of-the-art Methods: The research compares the proposed Modality- and Instance-aware Visual Prompt Learning (MIP) method with existing state-of-the-art techniques in VI ReID, showcasing superior performance across mainstream datasets like SYSU-MM01 and RegDB . The MIP method outperforms transformer-based methods like PMT, CMTR, DFLN-ViT, SPOT, and CNN-based methods like TOPLight, demonstrating advancements in VI ReID accuracy .

  4. Incorporation of Visual Prompt Learning in VI ReID: The paper explores the application of visual prompt learning in the VI ReID domain, drawing inspiration from successful implementations in other computer vision tasks . By leveraging specific prompts for different modalities and instances, the proposed approach aims to adapt original models effectively to VI ReID tasks, filling a gap in the current research landscape .

Overall, the paper's contributions lie in the development of a sophisticated Modality- and Instance-aware Visual Prompt Learning approach tailored for VI ReID, emphasizing the importance of prompt design, instance-specific adaptation, and performance benchmarking against existing methodologies in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of Visible-Infrared Person Re-identification (VI ReID) as highlighted in the provided document. Noteworthy researchers in this field include Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, and Peng Wang . These researchers have contributed to the development of solutions for VI ReID through their work on enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning.

The key to the solution mentioned in the paper involves utilizing modality-specific details to reveal potential relationships in Visible-Infrared Person Re-identification. The approach integrates modality-specific attributes like color, texture, and brightness to extract and explore potential relationships, which is crucial for addressing modality discrepancies in VI ReID tasks . Additionally, the paper discusses the use of visual prompt learning, inspired by textual prompts in NLP, to adapt original models to other tasks effectively, showcasing the potential of this approach in VI ReID .


How were the experiments in the paper designed?

The experiments in the paper were designed through a series of ablation studies and comparisons to evaluate the effectiveness of the proposed components and modules . The experiments started by validating the effectiveness of each component, followed by comparing the designed MPL and IPG modules with general prompt-based approaches . Additionally, the necessity of the generation-based design of the IPG module was discussed, and the effect of the MPL module on extracting implicit correspondence between modality-specific information was presented . These experiments were primarily conducted on the SYSU-MM01 dataset under the Single-Shot mode, unless specified otherwise .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is RegDB, which consists of 412 persons with visible and infrared images captured by respective cameras . The evaluation metrics include the Cumulative Matching Characteristic curve (CMC) and mean Average Precision (mAP) . The code for the proposed MIP method is implemented using the Pytorch framework . However, it is not explicitly mentioned in the provided context whether the code is open source or publicly available.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces a Modality-aware and Instance-aware Visual Prompts (MIP) network, which includes a Modality-aware Prompt Learning (MPL) module and an Instance-aware Prompt Generator (IPG) module . These components are designed to enhance person re-identification by leveraging modality-specific and instance-specific prompts to guide the model's adaptation and feature extraction process .

The experiments conducted in the paper include ablation studies to evaluate the effectiveness of the proposed components, such as the MPL module, IPG module, and IAEL loss . The results demonstrate the incremental performance improvements achieved by gradually adding these components to the baseline model, highlighting the importance of each component in enhancing the model's capabilities .

Furthermore, the paper compares the designed MPL and IPG modules with general prompt-based approaches, showcasing the superiority of the proposed approach in extracting implicit correspondence between modality-specific information and guiding the model's adaptation dynamically . This comparative analysis provides valuable insights into the effectiveness of the novel prompt learning strategy employed in the MIP network.

Overall, the experimental results presented in the paper offer compelling evidence to support the scientific hypotheses underlying the development and implementation of the Modality-aware and Instance-aware Visual Prompts (MIP) network for visible-infrared person re-identification . The thorough analysis and comparisons conducted in the experiments validate the efficacy of the proposed components and demonstrate their significant contributions to improving the model's performance in challenging re-identification tasks.


What are the contributions of this paper?

The contributions of the paper "Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning" include:

  • Introducing Modality- and Instance-aware Visual Prompt Learning: The paper introduces a novel approach that leverages modality- and instance-aware visual prompt learning to enhance visible-infrared person re-identification .
  • Improving Person Re-identification: The research aims to improve the matching of visible and infrared images of the same individuals across different camera views, focusing on utilizing both invariant information like shape and modality-specific details such as color for enhanced re-identification .
  • Addressing Modality-Specific Challenges: The study addresses challenges related to modality-specific features and details by incorporating modality-aware learning strategies into the re-identification process .
  • Enhancing Training with Valuable Information: The paper emphasizes the importance of utilizing valuable information from both visible and infrared modalities during training to enhance the person re-identification process .

What work can be continued in depth?

Further research in the field of Visible-Infrared Person Re-identification can be expanded in several directions based on the existing works:

  • Exploring Modality-Specific Features: Future studies can delve deeper into understanding and utilizing modality-specific features for person re-identification tasks, as highlighted by previous research .
  • Enhancing Visual Prompt Learning: There is potential to further optimize and refine visual prompt learning techniques for adapting models to various tasks, as demonstrated in computer vision tasks .
  • Investigating Cross-Modality Consistency: Research can focus on exploring and leveraging cross-modality consistency information to improve the performance of person re-identification models across different modalities .
  • Instance-Specific Adaptation: Future work can concentrate on developing more sophisticated methods for instance-specific adaptation in person re-identification models to capture discriminative clues for identification more effectively .
  • Utilizing Transformer Architectures: Given the success of transformer architectures in various tasks, there is a scope to further investigate the application of transformer-based models in Visible-Infrared Person Re-identification for improved performance .
  • Addressing Modality Discrepancies: Researchers can focus on developing innovative approaches to handle modality discrepancies effectively, such as feature alignment and fusion operations, to enhance the accuracy of person re-identification models .
Tables
5
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