68 citations · 241 across the 16 of their papers we have counts for
17 papers
Robust Cross-Modal Knowledge Distillation for Unconstrained Videos
Wenke Xia, Xingjian Li, Andong Deng +3
Cross-modal distillation has been widely used to transfer knowledge across different modalities, enriching the representation of the target unimodal one. Recent studies highly rela…
Towards Inadequately Pre-trained Models in Transfer Learning
Andong Deng, Xingjian Li, Di Hu +3
Pre-training has been a popular learning paradigm in deep learning era, especially in annotation-insufficient scenario. Better ImageNet pre-trained models have been demonstrated, f…
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
Konrad Heidler, Lichao Mou, Di Hu +5
Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In r…
Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation
Yapeng Tian, Di Hu, Chenliang Xu
There are rich synchronized audio and visual events in our daily life. Inside the events, audio scenes are associated with the corresponding visual objects; meanwhile, sounding obj…
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…
Listen to the Image
Di Hu, Dong Wang, Xuelong Li +2
Visual-to-auditory sensory substitution devices can assist the blind in sensing the visual environment by translating the visual information into a sound pattern. To improve the tr…
Dense Multimodal Fusion for Hierarchically Joint Representation
Di Hu, Feiping Nie, Xuelong Li
Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Hence, it is highly expected to learn effective joint rep…
Deep LDA Hashing
Di Hu, Feiping Nie, Xuelong Li
The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does. In this…
Deep Multimodal Clustering for Unsupervised Audiovisual Learning
Di Hu, Feiping Nie, Xuelong Li
The seen birds twitter, the running cars accompany with noise, etc. These naturally audiovisual correspondences provide the possibilities to explore and understand the outside worl…
Deep Binary Reconstruction for Cross-modal Hashing
Xuelong Li, Di Hu, Feiping Nie
With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the bin…
Image2song: Song Retrieval via Bridging Image Content and Lyric Words
Xuelong Li, Di Hu, Xiaoqiang Lu
Image is usually taken for expressing some kinds of emotions or purposes, such as love, celebrating Christmas. There is another better way that combines the image and relevant song…