Metric learning minimizes the gap between similar (positive) pairs of data
points and increases the separation of dissimilar (negative) pairs, aiming at
capturing the underlying data structure and enhancing the performance of tasks
like audio-visual cross-modal retrieval (AV-CMR). Recent works employ sampling
methods to select impactful data points from the embedding space during
training. However, the model training fails to fully explore the space due to
the scarcity of training data points, resulting in an incomplete representation
of the overall positive and negative distributions. In this paper, we propose
an innovative Anchor-aware Deep Metric Learning (AADML) method to address this
challenge by uncovering the underlying correlations among existing data points,
which enhances the quality of the shared embedding space. Specifically, our
method establishes a correlation graph-based manifold structure by considering
the dependencies between each sample as the anchor and its semantically similar
samples. Through dynamic weighting of the correlations within this underlying
manifold structure using an attention-driven mechanism, Anchor Awareness (AA)
scores are obtained for each anchor. These AA scores serve as data proxies to
compute relative distances in metric learning approaches. Extensive experiments
conducted on two audio-visual benchmark datasets demonstrate the effectiveness
of our proposed AADML method, significantly surpassing state-of-the-art models.
Furthermore, we investigate the integration of AA proxies with various metric
learning methods, further highlighting the efficacy of our approach.

提出了一种创新的 Anchor-aware Deep Metric Learning (AADML) 方法，通过揭示现有数据点之间的潜在相关性来提高共享嵌入空间的质量，并在两个音频 - 视觉基准数据集上的广泛实验证明了该方法的有效性，显著超过了 state-of-the-art 模型。