68 citations · 241 across the 16 of their papers we have counts for
7 papers · 1 filter
Temporal Relational Modeling with Self-Supervision for Action Segmentation
Dong Wang, Di Hu, Xingjian Li +1
Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) ha…
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Di Hu, Rui Qian, Minyue Jiang +5
Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a…
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Xingjian Li, Di Hu, Xuhong Li +5
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain b…
Multiple Sound Sources Localization from Coarse to Fine
Rui Qian, Di Hu, Heinrich Dinkel +3
How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations. To solve this proble…
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Di Hu, Lichao Mou, Qingzhong Wang +4
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail…
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Di Hu, Xuhong Li, Lichao Mou +5
Aerial scene recognition is a fundamental task in remote sensing and has recently received increased interest. While the visual information from overhead images with powerful model…
Curriculum Audiovisual Learning
Di Hu, Zheng Wang, Haoyi Xiong +3
Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data. In this paper, we present a flexible…