4 accepted papers
A central bottleneck in unsupervised cross-modal hashing is that both similarity guidance and objective weighting are typically static, while the semantic structure induced during training is inherently dynamic. We present DSSL-Hash, which turns semantic structure learning into an evolving process r
In unsupervised cross-modal hashing, real world multimodal data often exhibit partial alignment and semantic ambiguity. Dominant modalities can easily bias the fusion process, while semantically related samples may be mistakenly treated as negatives in contrastive learning, leading to unstable optim
In cross-modal retrieval tasks, unsupervised hash code learning still faces key challenges, including the difficulty of modeling shared semantic structures across modalities and the inability to adaptively balance multiple supervision objectives during optimization. To address these issues, we propo
Modelling musical performers’ individual playing styles based on audio features is important for music education, music expression analysis and music generation. In violin performance, the perception of playing styles are mainly affected by the characteristic musical timbre, which is mostly determin…