Search Results for author: Jiemin Fang

Found 28 papers, 19 papers with code

GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting

1 code implementation15 Feb 2024 Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian

Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.

Neural Rendering Object

Fast High Dynamic Range Radiance Fields for Dynamic Scenes

no code implementations11 Jan 2024 Guanjun Wu, Taoran Yi, Jiemin Fang, Wenyu Liu, Xinggang Wang

To extend HDR NeRF methods to wider applications, we propose a dynamic HDR NeRF framework, named HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures.

Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views

no code implementations7 Dec 2023 Yabo Chen, Jiemin Fang, YuYang Huang, Taoran Yi, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian

We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image.

Transparent objects

Segment Any 3D Gaussians

no code implementations1 Dec 2023 Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian

Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.

Interactive Segmentation Scene Understanding +1

GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions

no code implementations27 Nov 2023 Jiemin Fang, Junjie Wang, Xiaopeng Zhang, Lingxi Xie, Qi Tian

Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians.

3D scene Editing

4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

1 code implementation12 Oct 2023 Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, Xinggang Wang

Representing and rendering dynamic scenes has been an important but challenging task.

Segment Anything in 3D with Radiance Fields

1 code implementation NeurIPS 2023 Jiazhong Cen, Jiemin Fang, Zanwei Zhou, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian

The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.

Inverse Rendering Segmentation

TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors

no code implementations7 Apr 2023 Shaoyu Chen, Tianheng Cheng, Jiemin Fang, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang

Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.

object-detection Small Object Detection

WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation

1 code implementation3 Apr 2023 Lianghui Zhu, Yingyue Li, Jiemin Fang, Yan Liu, Hao Xin, Wenyu Liu, Xinggang Wang

Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects.

Decoder Weakly-supervised Learning +2

Generalizable Neural Voxels for Fast Human Radiance Fields

no code implementations27 Mar 2023 Taoran Yi, Jiemin Fang, Xinggang Wang, Wenyu Liu

Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem.

Novel View Synthesis

Fast Dynamic Radiance Fields with Time-Aware Neural Voxels

1 code implementation30 May 2022 Jiemin Fang, Taoran Yi, Xinggang Wang, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Matthias Nießner, Qi Tian

A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions.

Temporally Efficient Vision Transformer for Video Instance Segmentation

3 code implementations CVPR 2022 Shusheng Yang, Xinggang Wang, Yu Li, Yuxin Fang, Jiemin Fang, Wenyu Liu, Xun Zhao, Ying Shan

To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).

Instance Segmentation Semantic Segmentation +1

Exploring Complicated Search Spaces with Interleaving-Free Sampling

no code implementations5 Dec 2021 Yunjie Tian, Lingxi Xie, Jiemin Fang, Jianbin Jiao, Qixiang Ye, Qi Tian

In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}.

Neural Architecture Search

NeuSample: Neural Sample Field for Efficient View Synthesis

1 code implementation30 Nov 2021 Jiemin Fang, Lingxi Xie, Xinggang Wang, Xiaopeng Zhang, Wenyu Liu, Qi Tian

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.

Semantic-Aware Generation for Self-Supervised Visual Representation Learning

1 code implementation25 Nov 2021 Yunjie Tian, Lingxi Xie, Xiaopeng Zhang, Jiemin Fang, Haohang Xu, Wei Huang, Jianbin Jiao, Qi Tian, Qixiang Ye

In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.

Representation Learning Semantic Segmentation

Bag of Instances Aggregation Boosts Self-supervised Distillation

1 code implementation ICLR 2022 Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian

Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.

Contrastive Learning Linear evaluation +1

You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

2 code implementations NeurIPS 2021 Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu

Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure?

Object object-detection +1

ResizeMix: Mixing Data with Preserved Object Information and True Labels

1 code implementation21 Dec 2020 Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang

Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.

Data Augmentation Image Classification +3

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

2 code implementations21 Jun 2020 Jiemin Fang, Yuzhu Sun, Qian Zhang, Kangjian Peng, Yuan Li, Wenyu Liu, Xinggang Wang

In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.

Image Classification Neural Architecture Search +5

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search

no code implementations ICLR 2020 Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang

In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.

Image Classification Neural Architecture Search +4

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