Search Results for author: Guofeng Cui

Found 6 papers, 2 papers with code

Monocular 3D Object Detection via Feature Domain Adaptation

no code implementations ECCV 2020 Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu

Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches.

Domain Adaptation Foreground Segmentation +3

Differentiable Synthesis of Program Architectures

no code implementations NeurIPS 2021 Guofeng Cui, He Zhu

Differentiable programs have recently attracted much interest due to their interpretability, compositionality, and their efficiency to leverage differentiable training.

Program Synthesis

Talking-head Generation with Rhythmic Head Motion

1 code implementation16 Jul 2020 Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu

When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information.

Talking Head Generation

What comprises a good talking-head video generation?: A Survey and Benchmark

1 code implementation7 May 2020 Lele Chen, Guofeng Cui, Ziyi Kou, Haitian Zheng, Chenliang Xu

In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies.

Talking Head Generation Video Generation

Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation

no code implementations17 Nov 2019 Ziyi Kou, Guofeng Cui, Shaojie Wang, Wentian Zhao, Chenliang Xu

In this paper, we propose a confidence segmentation (ConfSeg) module that builds confidence score for each pixel in CAM without introducing additional hyper-parameters.

Object Weakly-Supervised Object Localization

Weakly Supervised Localization Using Background Images

no code implementations9 Sep 2019 Ziyi Kou, Wentian Zhao, Guofeng Cui, Shaojie Wang

Weakly Supervised Object Localization (WSOL) methodsusually rely on fully convolutional networks in order to ob-tain class activation maps(CAMs) of targeted labels.

Object Weakly-Supervised Object Localization

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