Search Results for author: Wenrui Ding

Found 10 papers, 3 papers with code

Editing 3D Scenes via Text Prompts without Retraining

no code implementations10 Sep 2023 Shuangkang Fang, Yufeng Wang, Yi Yang, Yi-Hsuan Tsai, Wenrui Ding, Shuchang Zhou, Ming-Hsuan Yang

To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining.

3D scene Editing 3D Scene Reconstruction +2

PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

1 code implementation8 Apr 2023 Shuangkang Fang, Yufeng Wang, Yi Yang, Weixin Xu, Heng Wang, Wenrui Ding, Shuchang Zhou

To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures.

3D Reconstruction Novel View Synthesis

Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies

1 code implementation3 Sep 2022 Xingrun Xing, Yangguang Li, Wei Li, Wenrui Ding, Yalong Jiang, Yufeng Wang, Jing Shao, Chunlei Liu, Xianglong Liu

Second, to improve the robustness of binary models with contextual dependencies, we compute the contextual dynamic embeddings to determine the binarization thresholds in general binary convolutional blocks.

Binarization Inductive Bias

A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

no code implementations8 Dec 2021 Xianlin Zeng, Yalong Jiang, Wenrui Ding, Hongguang Li, Yafeng Hao, Zifeng Qiu

High-level graph representations encode the trajectories of people and the interactions among multiple identities while low-level graph representations encode the local body postures of each person.

Anomaly Detection In Surveillance Videos

Semi-supervised Multi-task Learning for Semantics and Depth

no code implementations14 Oct 2021 Yufeng Wang, Yi-Hsuan Tsai, Wei-Chih Hung, Wenrui Ding, Shuo Liu, Ming-Hsuan Yang

Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.

Depth Estimation Multi-Task Learning +1

Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting

1 code implementation ECCV 2020 Xiyang Liu, Jie Yang, Wenrui Ding

The crowd counting task aims at estimating the number of people located in an image or a frame from videos.

Crowd Counting regression

GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

no code implementations25 Nov 2019 Chunlei Liu, Wenrui Ding, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Guodong Guo

The BGA method is proposed to modify the binary process of GBCNs to alleviate the local minima problem, which can significantly improve the performance of 1-bit DCNNs.

Face Recognition Object Recognition +1

Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation

no code implementations CVPR 2019 Chunlei Liu, Wenrui Ding, Xin Xia, Baochang Zhang, Jiaxin Gu, Jianzhuang Liu, Rongrong Ji, David Doermann

The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).

Aggregation Signature for Small Object Tracking

no code implementations24 Oct 2019 Chunlei Liu, Wenrui Ding, Jinyu Yang, Vittorio Murino, Baochang Zhang, Jungong Han, Guodong Guo

In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift.

Object Object Tracking

RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

no code implementations21 Aug 2019 Chunlei Liu, Wenrui Ding, Xin Xia, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Bohan Zhuang, Guodong Guo

Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications.

Binarization Object Tracking

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