Efficient Parameter Optimization for Visible-Infrared Person Re-Identification

Belangrijkste concepten

The authors propose a novel parameter hierarchical optimization (PHO) method to effectively train a visible-infrared person re-identification network. The method divides the network parameters into direct optimized and non-direct optimized parameters, allowing part of the parameters to be directly optimized without training, which reduces the search space and makes the training process easier.

Samenvatting

The paper addresses the task of visible-infrared person re-identification (VI-ReID), which aims to match pedestrian images captured by disjoint visible and infrared cameras. The authors propose a novel parameter hierarchical optimization (PHO) method to tackle this problem.
Key highlights:

The authors divide the network parameters into direct optimized and non-direct optimized parameters. The direct optimized parameters can be immediately optimized without training, which reduces the search space and makes the training process easier.
A self-adaptive alignment strategy (SAS) is introduced to automatically align the visible and infrared images through transformation. An auto-weighted alignment learning (AAL) module is also developed to automatically weight features according to their importance.
A cross-modality consistent learning (CCL) loss is established to extract discriminative person representations with translation consistency.
Extensive experiments on image-based and video-based VI-ReID datasets demonstrate the effectiveness and efficiency of the proposed PHO method.

Statistieken

The visible and infrared images have different imaging mechanisms, leading to large cross-modality discrepancies.
The intra-class variations arise from human pose changes, different viewpoints, background occlusions, etc.

Citaten

"Different from available methods, in this paper, we propose a novel parameter optimizing paradigm, parameter hierarchical optimization (PHO) method, for the task of VI-ReID."
"It allows part of parameters to be directly optimized without any training, which narrows the search space of parameters and makes the whole network more easier to be trained."

Belangrijkste Inzichten Gedestilleerd Uit

Parameter Hierarchical Optimization for Visible-Infrared Person Re-Identification

by Zeng YU,Yunx... om arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07930.pdf

Diepere vragen

How can the proposed PHO method be extended to other computer vision tasks beyond person re-identification

The proposed Parameter Hierarchical Optimization (PHO) method can be extended to other computer vision tasks beyond person re-identification by adapting the concept of hierarchical parameter optimization to different domains. For tasks like object detection, semantic segmentation, or image classification, the idea of dividing parameters into direct optimized and non-direct optimized categories can still be applied. By identifying specific parameters that can be optimized directly without extensive training, the search space can be reduced, making the optimization process more efficient. This approach can help in improving the training process and enhancing the performance of various computer vision models in tasks that require feature alignment, consistency learning, and optimization of network parameters.

What are the potential limitations of the direct parameter optimization approach, and how can they be addressed

One potential limitation of the direct parameter optimization approach is the risk of oversimplification or overlooking complex relationships within the network. Directly optimizing parameters without training may lead to suboptimal solutions or neglecting important nuances in the data. To address this limitation, it is essential to carefully design the criteria for determining which parameters can be optimized directly. Additionally, incorporating mechanisms for adaptive learning rates, regularization techniques, and validation checks can help prevent overfitting and ensure that the optimization process is robust and effective. Regular monitoring and validation of the optimized parameters can also help in identifying any discrepancies or inconsistencies that may arise from the direct optimization approach.

How can the cross-modality alignment and discriminative representation learning strategies be further improved to handle more challenging real-world scenarios

To further improve the cross-modality alignment and discriminative representation learning strategies for handling more challenging real-world scenarios, several enhancements can be considered:

Dynamic Alignment Strategies: Implementing dynamic alignment strategies that can adapt to varying degrees of cross-modality discrepancies in different scenarios. This could involve incorporating reinforcement learning techniques to adjust alignment parameters based on the input data.
Multi-Level Alignment: Introducing multi-level alignment mechanisms that can capture both global and local features for better alignment accuracy. This could involve hierarchical alignment processes that consider features at different scales.
Adversarial Learning: Integrating adversarial learning techniques to enhance the robustness of the alignment process and improve the discriminative representation learning. Adversarial training can help in generating more realistic and aligned feature representations.
Semi-Supervised Learning: Leveraging semi-supervised learning approaches to utilize unlabeled data for improving alignment and representation learning. Incorporating self-supervised or unsupervised learning methods can help in learning more generalized and robust representations.
Domain Adaptation: Exploring domain adaptation techniques to handle domain shifts and variations in real-world scenarios. By adapting the model to different domains or environments, the alignment and representation learning can be more effective in diverse settings.