83 citations · 146 across the 13 of their papers we have counts for
13 papers
GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video
Bruce X. B. Yu, Zhi Zhang, Yongxu Liu +3
3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimat…
Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems
Fengbo Lan, Chang Wen Chen
The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. Wh…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection
Ye Liu, Junsong Yuan, Chang Wen Chen
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of <human, action, object> in images. Most existin…
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos
Lifang Wu, Zhou Yang, Qi Wang +4
Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to ident…
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos
Lifang Wu, Zhou Yang, Jiaoyu He +4
In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collectiv…
AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
Guo-Jun Qi, Liheng Zhang, Chang Wen Chen +1
The learning of Transformation-Equivariant Representations (TERs), which is introduced by Hinton et al. \cite{hinton2011transforming}, has been considered as a principle to reveal…
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)
Shuang Ma, Jianlong Fu, Chang Wen Chen +1
Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing wo…
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
Shuang Ma, Chang Wen Chen
Exponential growth of social media consumption demands effective user interests-social contents modeling for more personalized recommendation and social media summarization. Howeve…
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
Shuang Ma, Jing Liu, Chang Wen Chen
Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is oft…
Computationally Recoverable Camouflage: A Universal Model for Privacy-Aware Location-Based Services
Changsha Ma, Zhisheng Yan, Chang Wen Chen
With the prevalence of location-based services (LBSs) supported by advanced positioning technology, there is a dramatic increase in the transmission of high-precision personal geog…
Attribute-Based Multi-Dimensional Scalable Access Control For Social Media Sharing
Changsha Ma, Chang Wen Chen
Media sharing is an extremely popular paradigm of social interaction in online social networks (OSNs) nowadays. The scalable media access control is essential to perform informatio…
Service Provisioning and Profit Maximization in Network-assisted Adaptive HTTP Streaming
Zhisheng Yan, Cedric Westphal, Xin Wang +1
Adaptive HTTP streaming with centralized consideration of multiple streams has gained increasing interest. It poses a special challenge that the interests of both content provider…