Temporal Knowledge Propagation for Image-to-Video Person Re-identification (1908.03885v3)

Abstract: In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin. Code is available at: https://github.com/guxinqian/TKP

Temporal Knowledge Propagation for Image-to-Video Person Re-identification

The paper "Temporal Knowledge Propagation for Image-to-Video Person Re-identification" introduces a novel methodology designed to enhance the task of Person Re-identification (Re-ID) in scenarios where the query is a single image, and the gallery consists of multiple surveillance videos. The primary challenge addressed in this paper is the information asymmetry that exists between still images and videos due to the lack of temporal information in images, which complicates the matching process. To address this, the authors propose a Temporal Knowledge Propagation (TKP) method that aims to transfer temporal knowledge from a video representation network to an image representation network.

The approach involves enforcing the image representation network to align with the outputs of the video network within a shared feature space. This alignment is achieved through backpropagation, allowing temporal knowledge to be transferred and augmenting the image features, thereby mitigating the information asymmetry. The model also incorporates classification and integrated triplet losses to learn discriminative and expressive features for both images and videos. The experiments conducted on two standard datasets demonstrate that the proposed TKP approach leads to significant improvements, surpassing the state-of-the-art by a considerable margin.

Key Contributions and Methodology

1. Temporal Knowledge Propagation (TKP) Method: The TKP method effectively transfers temporal knowledge from video frames to still images through a shared feature space. This is inspired by the principles of knowledge distillation, where a more robust representation, in this case, temporal, is transferred to enhance another representation.
2. Dual Network Strategy: The paper utilizes a dual network strategy where the image representation network is based on ResNet-50 and the video network incorporates non-local neural networks to model the temporal relationships across video frames. The non-local blocks are particularly crucial for capturing long-range dependencies, which enhances the robustness of the video representations.
3. Enhanced Feature Learning: By aligning the image features to match the video features incorporating temporal context, the image representation learns to be more robust and discriminative, addressing the core Re-ID challenge in image-to-video scenarios.
4. Integrated Loss Functions: The utilization of classification and integrated triplet loss functions further strengthens the feature representation by encouraging both modality alignment and discrimination within and across modalities.
5. Comprehensive Evaluation: The methodology was rigorously evaluated against contemporary techniques on the MARS and DukeMTMC-VideoReID datasets, showing remarkable improvements in both mAP and top-1 accuracy, with a noted enhancement of robustness in image features manifested through the transferred temporal knowledge.

Implications and Future Directions

This research has significant implications for real-world surveillance systems, particularly those involving the tracking of individuals across wide-spanning and crowded environments using sparse image queries. By reducing the information gap in temporal contexts between static images and dynamic videos, the proposed TKP method can significantly enhance the accuracy and reliability of person re-identification systems.

Theoretically, this paper advances the understanding of knowledge transfer in cross-modal learning settings. The TKP introduces a novel angle in knowledge distillation, specifically targeting cross-modal temporal knowledge propagation. For future exploration, the expansion of this methodology could encompass other domains where input modalities vary significantly in content richness, such as audio-to-video synchronization or sensor fusion tasks.

Moreover, extending the scope of TKP to accommodate real-time adjustments and improvements in dynamic environments, where query images and video galleries evolve continuously, could be an intriguing development avenue. The integration of more sophisticated temporal modeling techniques, such as recurrent networks or attention mechanisms, may further refine the alignment process, offering deeper insights and more robust cross-modal feature representations.

In conclusion, this paper contributes a compelling framework that significantly advances the field of person re-identification by effectively tackling the challenge of temporal knowledge asymmetry in image-to-video matching tasks.