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20162022
most citedStreaming Radiance Fields for 3D Video Synthesis

26 citations · 26 across the 3 of their papers we have counts for

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7 papers · 1 filter

cs.CV202331 cited

LGViT: Dynamic Early Exiting for Accelerating Vision Transformer

Guanyu Xu, Jiawei Hao, Li Shen +4

Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive…

cs.CV202226 cited

Streaming Radiance Fields for 3D Video Synthesis

Lingzhi Li, Zhen Shen, Zhongshu Wang +2

We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a singl…

cs.CV2022

Depth-Aware Generative Adversarial Network for Talking Head Video Generation

Fa-Ting Hong, Longhao Zhang, Li Shen +1

Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving vide…

cs.CV2021

Structure-Regularized Attention for Deformable Object Representation

Shenao Zhang, Li Shen, Zhifeng Li +1

Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks. Recent approaches that focus on modeling global context, such as…

cs.CV2018

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

Jie Hu, Li Shen, Samuel Albanie +2

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from captu…

cs.CV2018

Comparator Networks

Weidi Xie, Li Shen, Andrew Zisserman

The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to l…