Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification
arxiv(2024)
Abstract
Generalizable vehicle re-identification (ReID) aims to enable the
well-trained model in diverse source domains to broadly adapt to unknown target
domains without additional fine-tuning or retraining. However, it still faces
the challenges of domain shift problem and has difficulty accurately
generalizing to unknown target domains. This limitation occurs because the
model relies heavily on primary domain-invariant features in the training data
and pays less attention to potentially valuable secondary features. To solve
this complex and common problem, this paper proposes the two-stage Multi-expert
Knowledge Confrontation and Collaboration (MiKeCoCo) method, which incorporates
multiple experts with unique perspectives into Contrastive Language-Image
Pretraining (CLIP) and fully leverages high-level semantic knowledge for
comprehensive feature representation. Specifically, we propose to construct the
learnable prompt set of all specific-perspective experts by adversarial
learning in the latent space of visual features during the first stage of
training. The learned prompt set with high-level semantics is then utilized to
guide representation learning of the multi-level features for final knowledge
fusion in the next stage. In this process of knowledge fusion, although
multiple experts employ different assessment ways to examine the same vehicle,
their common goal is to confirm the vehicle's true identity. Their collective
decision can ensure the accuracy and consistency of the evaluation results.
Furthermore, we design different image inputs for two-stage training, which
include image component separation and diversity enhancement in order to
extract the ID-related prompt representation and to obtain feature
representation highlighted by all experts, respectively. Extensive experimental
results demonstrate that our method achieves state-of-the-art recognition
performance.
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