1 code implementation • 23 May 2025 • Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Wayne Xin Zhao
Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures.
no code implementations • 18 Apr 2025 • Yichen Liu
Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and calibration.
no code implementations • 13 Mar 2025 • Xiangjie Kong, Zhenghao Chen, Weiyao Liu, Kaili Ning, Lechao Zhang, Syauqie Muhammad Marier, Yichen Liu, Yuhao Chen, Feng Xia
However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets.
1 code implementation • CVPR 2025 • Jingcheng Ni, Yuxin Guo, Yichen Liu, Rui Chen, Lewei Lu, Zehuan Wu
The prevailing driving world model mainly build on video prediction model.
no code implementations • 20 Dec 2024 • Yichen Liu, Abhijit Dasgupta, Qiwei He
Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing.
1 code implementation • 6 Dec 2024 • Rui Chen, Zehuan Wu, Yichen Liu, Yuxin Guo, Jingcheng Ni, Haifeng Xia, Siyu Xia
The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system.
1 code implementation • 11 Nov 2024 • Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Xiangju Lu, Junmin Zhu, Wei zhang
As Large Language Models (LLMs) evolve in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become critical for real-world applications.
1 code implementation • 29 Oct 2024 • Letian Gong, Yan Lin, Xinyue Zhang, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, Huaiyu Wan
Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks.
no code implementations • 11 Oct 2024 • Cheng Qian, Xianglong Shi, Shanshan Yao, Yichen Liu, Fengming Zhou, Zishu Zhang, Junaid Akram, Ali Braytee, Ali Anaissi
We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations.
1 code implementation • 26 Aug 2024 • Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, Jianyu Chen
In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains.
no code implementations • 9 Aug 2024 • Yan Lin, Yichen Liu, Zeyu Zhou, Haomin Wen, Erwen Zheng, Shengnan Guo, Youfang Lin, Huaiyu Wan
To better utilize vehicle trajectories, it is essential to develop a trajectory learning approach that can effectively and efficiently extract rich semantic information, including movement behavior and travel purposes, to support accurate downstream applications.
no code implementations • 9 Aug 2024 • Yichen Liu, Penghui Du, Yi Liu Quanwei Zhang
This paper introduces Multi-Garment Customized Model Generation, a unified framework based on Latent Diffusion Models (LDMs) aimed at addressing the unexplored task of synthesizing images with free combinations of multiple pieces of clothing.
no code implementations • 8 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.
no code implementations • 22 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.
1 code implementation • 8 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.
no code implementations • 3 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.
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.
no code implementations • 21 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.
no code implementations • 13 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.
1 code implementation • 2 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.
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.
no code implementations • 22 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.
2 code implementations • CVPR 2023 • Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF.
no code implementations • 24 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.
no code implementations • 13 Jun 2022 • Xiaomin Liu, Yuli Chen, Yihao Zhang, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
We propose a physics-informed EDFA gain model based on the active learning method.
no code implementations • 7 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, has garnered significant attention.
no code implementations • 10 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.
no code implementations • 13 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.
no code implementations • 29 Sep 2021 • Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Yue Wang, Yang Yuan, Hang Zhao
We name this problem of multi-modal training, \emph{Modality Laziness}.
no code implementations • 21 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.
Ranked #72 on
Semantic Segmentation
on NYU Depth v2