216 citations · 319 across the 46 of their papers we have counts for
7 papers · 1 filter
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
Junbo Zhang, Guofan Fan, Guanghan Wang +3
Learning descriptive 3D features is crucial for understanding 3D scenes with diverse objects and complex structures. However, it is usually unknown whether important geometric attr…
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
Runpei Dong, Zekun Qi, Linfeng Zhang +5
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natura…
An empirical study of weakly supervised audio tagging embeddings for general audio representations
Heinrich Dinkel, Zhiyong Yan, Yongqing Wang +2
We study the usability of pre-trained weakly supervised audio tagging (AT) models as feature extractors for general audio representations. We mainly analyze the feasibility of tran…
UniKW-AT: Unified Keyword Spotting and Audio Tagging
Heinrich Dinkel, Yongqing Wang, Zhiyong Yan +2
Within the audio research community and the industry, keyword spotting (KWS) and audio tagging (AT) are seen as two distinct tasks and research fields. However, from a technical po…
Contrastive Deep Supervision
Linfeng Zhang, Xin Chen, Junbo Zhang +2
The success of deep learning is usually accompanied by the growth in neural network depth. However, the traditional training method only supervises the neural network at its last l…
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views
Junbo Zhang, Kaisheng Ma
A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the pre…