4 citations · 14 across the 20 of their papers we have counts for
5 papers · 1 filter
Metric Learning with Progressive Self-Distillation for Audio-Visual Embedding Learning
Donghuo Zeng, Kazushi Ikeda
Metric learning projects samples into an embedded space, where similarities and dissimilarities are quantified based on their learned representations. However, existing methods oft…
Anchor-aware Deep Metric Learning for Audio-visual Retrieval
Donghuo Zeng, Yanan Wang, Kazushi Ikeda +1
Metric learning minimizes the gap between similar (positive) pairs of data points and increases the separation of dissimilar (negative) pairs, aiming at capturing the underlying da…
Two-Stage Triplet Loss Training with Curriculum Augmentation for Audio-Visual Retrieval
Donghuo Zeng, Kazushi Ikeda
The cross-modal retrieval model leverages the potential of triple loss optimization to learn robust embedding spaces. However, existing methods often train these models in a singul…
MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio
Donghuo Zeng, Yi Yu, Keizo Oyama
This work present a music dataset named MusicTM-Dataset, which is utilized in improving the representation learning ability of different types of cross-modal retrieval (CMR). Littl…
Deep Learning of Human Perception in Audio Event Classification
Yi Yu, Samuel Beuret, Donghuo Zeng +1
In this paper, we introduce our recent studies on human perception in audio event classification by different deep learning models. In particular, the pre-trained model VGGish is u…