Deep supervised hashing has become a pivotal technique in large-scale image retrieval, offering significant benefits in terms of storage and search efficiency. However, existing deep supervised hashing models predominantly focus on generating fixed-length hash codes. This approach fails to address the inherent trade-off between efficiency and effectiveness when using hash codes of varying lengths. To determine the optimal hash code length for a specific task, multiple models must be trained for different lengths, leading to increased training time and computational overhead. Furthermore, the current paradigm overlooks the potential relationships between hash codes of different lengths, limiting the overall effectiveness of the models. To address these challenges, we propose the Nested Hash Layer (NHL), a plug-and-play module designed for existing deep supervised hashing models. The NHL framework introduces a novel mechanism to simultaneously generate hash codes of varying lengths in a nested manner. To tackle the optimization conflicts arising from the multiple learning objectives associated with different code lengths, we further propose an adaptive weights strategy that dynamically monitors and adjusts gradients during training. Additionally, recognizing that the structural information in longer hash codes can provide valuable guidance for shorter hash codes, we develop a long-short cascade self-distillation method within the NHL to enhance the overall quality of the generated hash codes. Extensive experiments demonstrate that NHL not only accelerates the training process but also achieves superior retrieval performance across various deep hashing models. Our code is publicly available at https://github.com/hly1998/NHL.

翻译：深度监督哈希已成为大规模图像检索中的一项关键技术，在存储和搜索效率方面具有显著优势。然而，现有的深度监督哈希模型主要专注于生成固定长度的哈希码。这种方法未能解决在使用不同长度哈希码时效率与效果之间固有的权衡问题。为了确定特定任务的最佳哈希码长度，必须针对不同长度训练多个模型，从而导致训练时间和计算开销增加。此外，当前范式忽视了不同长度哈希码之间潜在的关系，限制了模型的整体有效性。为了应对这些挑战，我们提出了嵌套哈希层（NHL），这是一个为现有深度监督哈希模型设计的即插即用模块。NHL框架引入了一种新颖的机制，以嵌套方式同时生成不同长度的哈希码。为了解决因不同码长关联的多个学习目标而产生的优化冲突，我们进一步提出了一种自适应权重策略，在训练过程中动态监控和调整梯度。此外，我们认识到较长哈希码中的结构信息可以为较短哈希码提供有价值的指导，因此在NHL内部开发了一种长短级联自蒸馏方法，以提升生成哈希码的整体质量。大量实验表明，NHL不仅加速了训练过程，还在多种深度哈希模型中实现了卓越的检索性能。我们的代码公开在 https://github.com/hly1998/NHL。