3 accepted papers
In this paper, we study deep unsupervised hashing, a critical problem for approximate nearest neighbor research. Most recent methods solve this problem by semantic similarity reconstruction for guiding hashing network learning or contrastive learning of hash codes. However, in multi-label scenarios,…
Multi-modal emotion recognition aims to extract sentiment-related information from multiple sources and integrate different modal representations for sentiment analysis. Alignment is an effective strategy to achieve semantically consistent representations for multi-modal emotion recognition, while t…
Many studies on automatic speech emotion recognition (SER) have been devoted to extracting meaningful emotional features for generating emotion-relevant representations. However, they generally ignore the complementary learning of static and dynamic features, leading to limited performances. In this…