Search Results for author: Yuhang Liu

Found 21 papers, 4 papers with code

Identifiable Latent Neural Causal Models

no code implementations23 Mar 2024 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.

Representation Learning

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models

no code implementations9 Feb 2024 Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.

Representation Learning

Detection-based Intermediate Supervision for Visual Question Answering

no code implementations26 Dec 2023 Yuhang Liu, Daowan Peng, Wei Wei, Yuanyuan Fu, Wenfeng Xie, Dangyang Chen

Recently, neural module networks (NMNs) have yielded ongoing success in answering compositional visual questions, especially those involving multi-hop visual and logical reasoning.

Logical Reasoning Question Answering +1

CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts

no code implementations28 Nov 2023 Yichao Cai, Yuhang Liu, Zhen Zhang, Javen Qinfeng Shi

To address this limitation, we adopt a causal generative perspective for multimodal data and propose contrastive learning with data augmentation to disentangle content features from the original representations.

Contrastive Learning Image Augmentation +1

MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

no code implementations24 Oct 2023 Yizhe Yang, Huashan Sun, Jiawei Li, Runheng Liu, Yinghao Li, Yuhang Liu, Heyan Huang, Yang Gao

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.

Language Modelling Large Language Model

Identifiable Latent Polynomial Causal Models Through the Lens of Change

no code implementations24 Oct 2023 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.

Representation Learning

HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V

no code implementations27 Sep 2023 Yuhang Liu, Boyi Sun, Yuke Li, Yuzheng Hu, Fei-Yue Wang

It uses a graph-attention Transformer to extract domain-specific features for each agent, coupled with a cross-attention mechanism for the final fusion.

Autonomous Driving Graph Attention

Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier

1 code implementation22 Jul 2023 Zhixing Zhang, Ziwei Zhao, Dong Wang, Shishuang Zhao, Yuhang Liu, Jia Liu, LiWei Wang

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases.

Anatomy

PointScatter: Point Set Representation for Tubular Structure Extraction

1 code implementation13 Sep 2022 Dong Wang, Zhao Zhang, Ziwei Zhao, Yuhang Liu, Yihong Chen, LiWei Wang

Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task.

Segmentation

Identifying Weight-Variant Latent Causal Models

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.

Representation Learning

Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

no code implementations30 Aug 2022 Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi

Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.

Domain Adaptation

Truncated Matrix Power Iteration for Differentiable DAG Learning

1 code implementation30 Aug 2022 Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Declaration-based Prompt Tuning for Visual Question Answering

1 code implementation5 May 2022 Yuhang Liu, Wei Wei, Daowan Peng, Feida Zhu

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e. g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e. g., VQA) via a brand-new objective function, e. g., answer prediction.

Image-text matching Language Modelling +5

Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

no code implementations21 May 2021 Yuhang Liu, Fandong Zhang, Chaoqi Chen, Siwen Wang, Yizhou Wang, Yizhou Yu

In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.

Anatomy Decision Making +2

Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection

no code implementations CVPR 2020 Yuhang Liu, Fandong Zhang, Qianyi Zhang, Siwen Wang, Yizhou Wang, Yizhou Yu

In this paper, we introduce bipartite graph convolutional network to endow existing methods with cross-view reasoning ability of radiologists in mammogram mass detection.

On Closed 6-Manifolds Admitting Riemannian Metrics with Positive Sectional Curvature and Non-Abelian Symmetry

no code implementations6 May 2019 Yuhang Liu

We show that their Euler characteristic agrees with that of the known examples, i. e. $S^6$, $\mathbb{CP}^3$, the Wallach space $SU(3)/T^2$ and the biquotient $SU(3)//T^2$.

Differential Geometry 53C21

Variational Bayesian Dropout with a Hierarchical Prior

no code implementations CVPR 2019 Yuhang Liu, Wenyong Dong, Lei Zhang, Dong Gong, Qinfeng Shi

Then, we incorporate such a prior into inferring the joint posterior over network weights and the variance in the hierarchical prior, with which both the network training and the dropout rate estimation can be cast into a joint optimization problem.

Deblurring Natural Image Using Super-Gaussian Fields

no code implementations ECCV 2018 Yuhang Liu, Wenyong Dong, Dong Gong, Lei Zhang, Qinfeng Shi

Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e. g., image gradients), which are insufficient to capture the complicated image structures.

Blind Image Deblurring Image Deblurring

FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction

no code implementations7 Sep 2017 Yao Tang, Fei Gao, Jufu Feng, Yuhang Liu

In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning.

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