DSCH-Loss: A Dynamic Semantic Channel Objective for Deep Semantic Hashing

2026-07-27

AI Summary

This work addresses a key limitation in existing deep semantic hashing methods, where fixed-width and fixed-position semantic channels induce discontinuities in the loss function, thereby hindering optimization. To overcome this, the authors propose the Dynamic Semantic Channel Hashing (DSCH) loss, which dynamically adjusts both the position and scale of semantic channels to yield a smoother loss landscape and enhance hash code learning. Additionally, they introduce a tie-aware mean Average Precision (mAP) metric to more accurately evaluate retrieval performance under discrete Hamming distances. Evaluated across both cross-modal and single-modal settings on two benchmark datasets using two distinct architectures, DSCH significantly outperforms current state-of-the-art methods in 35 out of 40 tasks, achieving up to a 1.75 percentage point improvement in tie-aware mAP.

Abstract

Semantic hashing methods for generating short binary hash codes that allow efficient approximate nearest neighbor search in high-dimensional data spaces have gained extensive consideration in recent years. Deep learning-based methods offer better semantic capturing capabilities than traditional approaches relying on manual feature engineering. Moreover, they enable a data-driven approach to semantic hashing across diverse data modalities, yielding high-quality cross-modal hash codes within a shared Hamming space. Previous work investigated the properties of this Hamming space and introduced a loss function based on predefined so-called semantic channels with fixed width and Hamming distances derived from label similarities. However, this formulation also introduced discontinuities into the loss landscape, complicating optimization. Based on these observations, we propose a newly designed loss function, Dynamic Semantic Channel Hashing (DSCH), using dynamically sized and positioned semantic channels in order to avoid loss landscape discontinuities. Furthermore, we endorse the use of tie-aware Mean Average Precision (mAP) as evaluation metric as it addresses the ambiguity in sample retrieval ordering, which emerges from the discreteness of hash code distances. Finally, multiple experimental settings conducted on two popular datasets and incorporating two different model architectures provide strong evidence that training using the DSCH objective outperforms training using other state-of-the-art loss functions. In a total of 35 out of 40 cross-modal and intra-modal retrieval tasks, models trained with DSCH achieve significantly higher tie-aware mAP scores across all four tested hash code lengths, showing compelling results across model architecture and used dataset. The mAP score uplifts are consistent and amount up to 1.75 percentage points compared to the respective second best.

Problem

• semantic hashing
• loss function
• Hamming space
• optimization discontinuity
• cross-modal retrieval

Innovation

• Dynamic Semantic Channel
• Semantic Hashing
• Deep Learning
• Tie-aware mAP
• Loss Function