Meta Batch-Instance Normalization for Generalizable Person Re-Identification (2011.14670v2)

Abstract: Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://github.com/bismex/MetaBIN.

Meta Batch-Instance Normalization for Person Re-Identification

Person re-identification (Re-ID), a critical component in surveillance systems, is the process of recognizing individuals across various cameras and environments. While supervised methodologies in this domain have achieved commendable accuracy, their effectiveness drastically reduces when exposed to new, unseen environments. Addressing this shortfall, the concept of domain generalization has gained traction, intended to enhance the transferability of Re-ID models beyond the contexts they were originally trained on. In this paper, the authors propose a novel framework named Meta Batch-Instance Normalization (MetaBIN) that seeks to advance generalizable Re-ID frameworks.

MetaBIN Framework

The paper addresses the inherent limitations of batch normalization (BN) and instance normalization (IN). BN attempts to learn discriminative features based on style variations within mini-batches but struggles with styles not encountered during training. Conversely, IN effectively normalizes individual style information but at the cost of potentially filtering out critical discriminative details. To bridge these gaps, the authors introduce MetaBIN, which integrates meta-learning techniques with batch-instance normalization layers to proactively simulate and address generalization failures.

Key Concepts

1. Learnable Batch-Instance Normalization: MetaBIN combines BN and IN with a learnable parameter that adjusts the balance between the two normalization strategies. This parameter is modulated through a meta-learning process, ensuring the network avoids overfitting to source domain samples and improves its robustness against unseen styles.
2. Meta-Learning Pipeline: The framework employs a meta-learning strategy wherein unsuccessful generalization scenarios are deliberately simulated. This is achieved by dividing source domain data into meta-train and meta-test sets, mimicking domain shifts anticipated during real-world application. The learnings from these simulations help refine the normalization layers.
3. Cyclic Inner-Updating: MetaBIN incorporates a cyclic adjustment of the learning rate during training cycles to diversify the virtual simulations and enhance the framework's adaptability.

Results and Contributions

The experimental evaluations demonstrate that MetaBIN considerably outperforms existing Re-ID methodologies. It achieves state-of-the-art results in large-scale domain generalization benchmarks and cross-domain Re-ID settings, validated across various unseen datasets. The results underscore the framework's robustness in transferring learned knowledge to novel environments without necessitating complex network architecture changes or additional data augmentations.

Implications

This paper's contributions serve both practical and theoretical advances in the field of person Re-ID. Practically, MetaBIN can be integrated into surveillance systems, enhancing their reliability across varied and unpredictable environments. Theoretically, the concept of simulating generalization failures to inform normalization strategies paves the way for future explorations in domain generalization and robust AI deployment.

Future Directions

Looking ahead, the fusion of meta-learning techniques with innovative normalization strategies like MetaBIN promises meaningful advances. Future research may explore the scalability of this approach across different AI applications, such as facial recognition or autonomous monitoring systems, to test its adaptability beyond person Re-ID. Additionally, investigating other normalization techniques and meta-learning paradigms could deepen our understanding of overcoming domain-specific limitations typical of supervised models.