学习生成外观先验以实现细粒度图像检索
Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval
AI总结：
本文提出GAPan网络，通过生成外观先验对齐方法，改进细粒度图像检索的泛化能力，提升对未见类别的检索性能。
摘要：
细粒度图像检索（FGIR）通常依赖已见类别的监督来学习判别嵌入以检索未见类别。然而，这种监督常使检索模型偏向已见类别的语义而非跨类别的底层外观特征，从而限制了对未见类别的检索性能。为此，我们提出GAPan，一种生成外观先验对齐网络，将学习目标从类别预测转向外观建模。技术上，GAPan使用基于归一化流的可逆密度模型处理检索特征。在正向方向，流将所有实例特征映射到潜在密度空间，其中每个已见类别由类条件高斯先验建模并通过精确似然估计优化。此方法通过利用流的可逆性质保留更丰富的外观细节。在反向方向，从这些学习先验的高密度区域采样并映射回特征空间，生成反映同类变异的外观感知锚点。这些锚点监督一种由先验驱动的对齐目标，使检索嵌入与类别特定的外观分布对齐，从而提升对未见类别的泛化能力。评估显示，GAPan在广泛使用的细粒度和粗粒度基准上均达到最先进的性能。
英文摘要：
Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervision often biases retrieval models toward the semantics of seen categories rather than the underlying appearance characteristics that generalize across categories, thereby limiting retrieval performance on unseen categories. To tackle this, we propose GAPan, a Generative Appearance Prior alignment network that reformulates the learning objective from category prediction toward appearance modeling. Technically, GAPan treats retrieval features with an invertible density model based on normalizing flows. In the forward direction, the flow maps all instance features into a latent density space, where each seen category is modeled by a class-conditional Gaussian prior and optimized via exact likelihood estimation. This formulation preserves richer appearance details by leveraging the invertible property of the flows. In the reverse direction, samples from the high-density regions of these learned priors are mapped back to the feature space to produce appearance-aware anchors that reflect intra-category variation. These anchors supervise a prior-driven alignment objective that aligns retrieval embeddings with category-specific appearance distributions, thereby improving generalization to unseen categories. Evaluations demonstrate that our GAPan achieves state-of-the-art performance on both widely-used fine- and coarse-grained benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
