Search Results for author: Yichen Liu

Found 18 papers, 4 papers with code

VP-LLM: Text-Driven 3D Volume Completion with Large Language Models through Patchification

no code implementations8 Jun 2024 Jianmeng Liu, Yichen Liu, Yuyao Zhang, Zeyuan Meng, Yu-Wing Tai, Chi-Keung Tang

Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction.

Neural Radiance Field in Autonomous Driving: A Survey

no code implementations22 Apr 2024 Lei He, Leheng Li, Wenchao Sun, Zeyu Han, Yichen Liu, Sifa Zheng, Jianqiang Wang, Keqiang Li

To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.

3D Reconstruction Autonomous Driving +2

DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker

1 code implementation8 Apr 2024 Jiapeng Wu, Yichen Liu

Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects.

Multi-Object Tracking Object +1

DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models

no code implementations3 Jan 2024 Yichen Liu, Huajian Zhang, Daqing Gao

Object detection models represented by YOLO series have been widely used and have achieved great results on the high quality datasets, but not all the working conditions are ideal.

Denoising object-detection +1

SANeRF-HQ: Segment Anything for NeRF in High Quality

no code implementations CVPR 2024 Yichen Liu, Benran Hu, Chi-Keung Tang, Yu-Wing Tai

Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis.

Novel View Synthesis Object +4

centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation

no code implementations21 Aug 2023 Xiaona Sun, Zhenyu Wu, Yichen Liu, Saier Hu, ZhiQiang Zhan, Yang Ji

Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.

Robust classification Unsupervised Domain Adaptation

Building a digital twin of EDFA: a grey-box modeling approach

no code implementations13 Jul 2023 Yichen Liu, Xiaomin Liu, Yihao Zhang, Meng Cai, Mengfan Fu, Xueying Zhong, Lilin Yi, Weisheng Hu, Qunbi Zhuge

To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required.

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

1 code implementation2 May 2023 Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan, Yue Wang, Yang Yuan, Hang Zhao

We abstract the features (i. e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions.

Instance Neural Radiance Field

1 code implementation ICCV 2023 Yichen Liu, Benran Hu, Junkai Huang, Yu-Wing Tai, Chi-Keung Tang

This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as {\bf \inerflong}, or \inerf.

3D Instance Segmentation Panoptic Segmentation +1

ONeRF: Unsupervised 3D Object Segmentation from Multiple Views

no code implementations22 Nov 2022 Shengnan Liang, Yichen Liu, Shangzhe Wu, Yu-Wing Tai, Chi-Keung Tang

We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations.

3D scene Editing Object +1

A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification

no code implementations24 Jun 2022 Yihao Zhang, Xiaomin Liu, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge

Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression.


Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding

no code implementations7 Jun 2022 Yichen Liu, Jiawei Chen, Defang Chen, Zhehui Zhou, Yan Feng, Can Wang

Knowledge Graph Embedding (KGE), which projects entities and relations into continuous vector spaces, have garnered significant attention.

Knowledge Graph Embedding Knowledge Graphs +3

Learning Visual Styles from Audio-Visual Associations

no code implementations10 May 2022 Tingle Li, Yichen Liu, Andrew Owens, Hang Zhao

Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization.

Image Stylization

Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension

no code implementations13 Dec 2021 Shusheng Xu, Yichen Liu, Xiaoyu Yi, Siyuan Zhou, Huizi Li, Yi Wu

We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.

Common Sense Reasoning Machine Reading Comprehension

Improving Multi-Modal Learning with Uni-Modal Teachers

no code implementations21 Jun 2021 Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, Hang Zhao

We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.

Image Segmentation Semantic Segmentation

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