Search Results for author: Xiaoming Zhao

Found 16 papers, 7 papers with code

PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing

no code implementations11 Nov 2024 Yiwen Duan, Yonghong Yu, Xiaoming Zhao, Yichang Wu, Wenbo Liu

Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks.

Bug fixing Code Generation

NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows

1 code implementation CVPR 2024 Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, Alexander Schwing

We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene.

IllumiNeRF: 3D Relighting Without Inverse Rendering

no code implementations10 Jun 2024 Xiaoming Zhao, Pratul P. Srinivasan, Dor Verbin, Keunhong Park, Ricardo Martin Brualla, Philipp Henzler

Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination -- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images.

Inverse Rendering Object

Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model

1 code implementation1 Jun 2024 Jinyin Chen, Xiaoming Zhao, Haibin Zheng, Xiao Li, Sheng Xiang, Haifeng Guo

Knowledge distillation (KD) is one of the widely used compression techniques for edge deployment, by obtaining a lightweight student model from a well-trained teacher model released on public platforms.

Knowledge Distillation Model Compression

Pseudo-Generalized Dynamic View Synthesis from a Video

no code implementations12 Oct 2023 Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M. Susskind, Alexander G. Schwing

In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.

Novel View Synthesis

Occupancy Planes for Single-view RGB-D Human Reconstruction

1 code implementation4 Aug 2022 Xiaoming Zhao, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing

Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location.

3D Human Reconstruction

Initialization and Alignment for Adversarial Texture Optimization

no code implementations28 Jul 2022 Xiaoming Zhao, Zhizhen Zhao, Alexander G. Schwing

While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature.

Texture Synthesis

ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

2 code implementations6 Dec 2021 Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter C. Y. Chen, Zhengguo Li

The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization.

Camera Pose Estimation Homography Estimation +2

Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network

no code implementations17 Nov 2021 Xiaoming Zhao, Jingmeng Liu, Xingming Wu, Weihai Chen, Fanghong Guo, Zhengguo Li

Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on.

Graph Neural Network Image Stitching +2

Stochastic Variance Reduction for Deep Q-learning

no code implementations20 May 2019 Wei-Ye Zhao, Xi-Ya Guan, Yang Liu, Xiaoming Zhao, Jian Peng

Recent advances in deep reinforcement learning have achieved human-level performance on a variety of real-world applications.

Deep Reinforcement Learning Q-Learning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.