Robust Self-paced Hashing For Cross-modal Retrieval With Noisy Labels

Abstract

Cross-modal hashing (CMH) has appeared as a popular technique for cross-modal retrieval due to its low storage cost and high computational efficiency in large-scale data. Most existing methods implicitly assume that multi-modal data is correctly labeled, which is expensive and even unattainable due to the inevitable imperfect annotations (i.e., noisy labels) in real-world scenarios. Inspired by human cognitive learning, a few methods introduce self-paced learning (SPL) to gradually train the model from easy to hard samples, which is often used to mitigate the effects of feature noise or outliers. It is a less-touched problem that how to utilize SPL to alleviate the misleading of noisy labels on the hash model. To tackle this problem, we propose a new cognitive cross-modal retrieval method called Robust Self-paced Hashing with Noisy Labels (RSHNL), which can mimic the human cognitive process to identify the noise while embracing robustness against noisy labels. Specifically, we first pr

Stats

• citations10
• S2 citations—
• github stars0
• HF likes0
• heat score7.81
• arxiv keypu2025robust

Related papers

• Semantic-consistent Bidirectional Contrastive Hashing For Noisy Multi-label Cross-modal Retrieval (2025)0.00
• Prompthash: Affinity-prompted Collaborative Cross-modal Learning For Adaptive Hashing Retrieval (2025)7.70
• Weakly-paired Cross-modal Hashing (2019)0.00
• Neighbor-aware Instance Refining With Noisy Labels For Cross-modal Retrieval (2025)2.26
• Ranking-based Deep Cross-modal Hashing (2019)13.34
• Adaptive Asymmetric Label-guided Hashing For Multimedia Search (2022)0.00
• Deep Class-guided Hashing For Multi-label Cross-modal Retrieval (2024)6.20
• RREH: Reconstruction Relations Embedded Hashing For Semi-paired Cross-modal Retrieval (2024)2.26