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<h2><hr><a name="cd"></a>Causal Discovery & Inference </h2>
<ul>
<li><p>
A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error. [<A HREF="https://openreview.net/pdf?id=S46Knicu56">PDF</A>]<br>
E. Gao, H. Bondell, W. Huang, and <b>M. Gong</b>.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
Identifiable Latent Polynomial Causal Models through the Lens of Change. [<A HREF="https://openreview.net/pdf?id=ia9fKO1Vjq">PDF</A>]<br>
Y. Liu, Z. Zhang, D. Gong, <b>M. Gong</b>, B. Huang, A. Hengel, K. Zhang, and J. Shi.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. [<A HREF="">PDF</A>] <br>
W. Liu, B. Huang, E. Gao, Q. Ke, H. Bondell, and <b>M. Gong</b>.<br>
In <a href="https://cclear.cc/"> CLeaR</a>, 2024.
</p></li>
<li><p>
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. [<A HREF="https://openreview.net/pdf?id=zsOOqjaj2z">PDF</A>]<br>
Y. Wang, X. Geng, W. Huang, B. Huang, <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
FedDAG: Federated DAG Structure Learning. [<A HREF="https://openreview.net/pdf?id=MzWgBjZ6Le">PDF</A>][<A HREF="https://github.com/ErdunGAO/FedDAG">CODE</A>]<br>
E. Gao, J. Chen, L. Shen, T. Liu, <b>M. Gong</b>, H. Bondell.<br>
In <a href="https://jmlr.org/tmlr/"> TMLR</a>, 2023.
</p></li>
<li><p>
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. [<A HREF="https://arxiv.org/pdf/2205.13869.pdf">PDF</A>][<A HREF="https://github.com/ErdunGAO/MissDAG">CODE</A>]<br>
E. Gao*, I. Ng*, <b>M. Gong</b>, L. Shen, W. Huang, T. Liu, K. Zhang, H. Bondell.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
Truncated Matrix Power Iteration for Differentiable DAG Learning. [<A HREF="https://arxiv.org/pdf/2208.14571">PDF</A>]<br>
Z. Zhang, I. Ng, D. Gong, Y. Liu, E.M. Abbasnejad, <b>M. Gong</b>, K. Zhang, J.Q. Shi.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
Causal Discovery from Non-Identical Variable Sets. [<A HREF="https://www.aaai.org/Papers/AAAI/2020GB/AAAI-HuangB.6175.pdf">PDF</A>]<br>
B. Huang, K. Zhang, <b>M. Gong</b>, and C. Glymour.<br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai20.php"> AAAI</a>, 2020.
</p></li>
<li><p>
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery. [<A HREF="https://papers.nips.cc/paper/8912-likelihood-free-overcomplete-ica-and-applications-in-causal-discovery.pdf">PDF</A>][<A HREF="https://github.com/dingchenwei/Likelihood-free_OICA">CODE</A>] <br>
C. Ding, <b>M. Gong</b>, K. Zhang, and D. Tao.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019. (<font color="red">Spotlight, acceptance rate 2.4%</font>)
</p></li>
<li><p>
Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. [<A HREF="https://papers.nips.cc/paper/9506-specific-and-shared-causal-relation-modeling-and-mechanism-based-clustering.pdf">PDF</A>]<br>
B. Huang, K. Zhang, P. Xie, <b>M. Gong</b>, E. P. Xing, and C. Glymour.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019.
</p></li>
<li><p>
Discovery and Forecasting in Nonstationary Environments with State-Space Models. [<A HREF="http://proceedings.mlr.press/v97/huang19g/huang19g.pdf">PDF</A>][<A HREF="http://proceedings.mlr.press/v97/huang19g/huang19g-supp.pdf">SUPP</A>][<A HREF="https://github.com/Biwei-Huang/Causal-discovery-and-forecasting-in-nonstationary-environments">CODE</A>]<br>
B. Huang, K. Zhang, <b>M. Gong</b>, and C. Glymour.<br>
In <a href="https://icml.cc/Conferences/2019">ICML</a></i>, 2019.
</p></li>
<li><p>
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. [<A HREF="http://auai.org/uai2018/proceedings/papers/372.pdf">PDF</A>]<br>
K. Zhang, <b>M. Gong</b>, J. Ramsey, K. Batmanghelich, P. Spirtes, and C. Glymour.<br>
In <a href="http://auai.org/uai2018/">UAI</a></i>, 2018. (<font color="red">Oral, acceptance rate 8.9%</font>)
</p></li>
<li><p>
Causal Discovery from Temporally Aggregated Time Series. [<A HREF="papers/UAI_CDTA.pdf">PDF</A>]<br>
<b>M. Gong</b>, K. Zhang, B. Schölkopf, C. Glymour, and D. Tao.<br>
In <a href="http://auai.org/uai2017/">UAI</a></i>, 2017.
</p></li>
<li><p>
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. [<A HREF="https://arxiv.org/pdf/1411.3972.pdf">PDF</A>][<A HREF="http://pgeiger.org/dl/software/CLH.zip">CODE</A>]<br>
P. Geiger, K. Zhang, <b> M. Gong</b>, B. Schölkopf, and D. Janzing.<br>
In <a href="https://icml.cc/2015/index.html">ICML</a></i>, 2015.
<li><p>
Discovering Temporal Causal Relations from Subsampled Data. [<A HREF="papers/ICML_SUBSAMPLE.pdf">PDF</A>][<A HREF="papers/CRSD.zip">CODE</A>] <br>
<b>M. Gong</b>*, K. Zhang*, B. Schölkopf, D. Tao, and P. Geiger.<br>
In <a href="https://icml.cc/2015/index.html">ICML</a></i>, 2015.
</p></li>
</ul>
<h2><hr><a name="cd"></a>Causal Representation Learning (Transfer, Robustness, Fairness, etc) </h2>
<ul>
<li><p>
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach. [<A HREF="https://openreview.net/pdf?id=SKulT2VX9p">PDF</A>]<br>
A. Zuo, Y. Li, S. Wei, and <b>M. Gong</b>.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
Improving Non-Transferable Representation Learning by Harnessing Content and Style. [<A HREF="https://openreview.net/pdf?id=FYKVPOHCpE">PDF</A>]<br>
Z. Hong, Z. Wang, L. Shen, Y. Yao, Z. Huang, S. Chen, C. Yang, <b>M. Gong</b>, and T. Liu.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Learning World Models with Identifiable Factorization. [<A HREF="https://arxiv.org/abs/2306.06561">PDF</A>]<br>
Y. Liu, B. Huang, Z. Zhu, H. Tian, <b>M. Gong</b>, Y. Yu, K. Zhang.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation. [<A HREF="">PDF</A>]<br>
Y. Lin, Y. Yao, X. Shi, <b>M. Gong</b>, X. Shen, D. Xu, T. Liu.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?. [<A HREF="https://openreview.net/forum?id=sDCMrYnXNGY">PDF</A>]<br>
Y. Yao, <b>M. Gong</b>, Y. Du, J. Yu, B. Han, K. Zhang, and T. Liu.<br>
In <a href="https://icml.cc/Conferences/2023"> ICML</a>, 2023.
</p></li>
<li><p>
Multi-Domain Image Generation and Translation with Identifiability Guarantees. [<A HREF="https://openreview.net/forum?id=U2g8OGONA_V">PDF</A>][<A HREF="https://github.com/Mid-Push/i-stylegan">CODE</A>]<br>
S. Xie, L. Kong, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. [<A HREF="https://arxiv.org/abs/2207.03162">PDF</A>]<br>
Z. Huang, X. Xia, L. Shen, B. Han, <b>M. Gong</b>, C. Gong, and T. Liu.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023.
</p></li>
<li><p>
Counterfactual Fairness with Partially Known Causal Graph. [<A HREF="https://arxiv.org/pdf/2205.13972">PDF</A>]<br>
A. Zuo, S. Wei, T. Liu, B. Han, K. Zhang, <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
Fair Classification with Instance-dependent Label Noise. [<A HREF="https://openreview.net/forum?id=s-pcpETLpY">PDF</A>] <br>
S. Wu, <b>M. Gong</b>, B. Han, Y. Liu, T. Liu.<br>
In <a href="https://cclear.cc/"> CLeaR</a>, 2022.
</p></li>
<li><p>
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning. [<A HREF="https://openreview.net/forum?id=YRq0ZUnzKoZ">PDF</A>][<A href="https://github.com/CR-Gjx/RIA">CODE</A>] <br>
J. Guo, <b>M. Gong</b>, D. Tao.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
CausalAdv: Adversarial Robustness Through the Lens of Causality. [<A HREF="https://openreview.net/forum?id=cZAi1yWpiXQ">PDF</A>] [<A href="https://github.com/YonggangZhangUSTC/CausalAdv" >CODE</A>] <br>
Y. Zhang, <b>M. Gong</b>, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?. [<A HREF="https://openreview.net/forum?id=zdmF437BCB">PDF</A>] [<a href="https://github.com/DMIRLAB-Group/DSAN">CODE</a>]<br>
P. Stojanov, Z. Li, <b>M. Gong</b>, R. Cai, J.G. Carbonell, and K. Zhang.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2021.
</p></li>
<li><p>
Instance-dependent Label-noise Learning under a Structural Causal Model. [<A HREF="https://arxiv.org/abs/2109.02986">PDF</A>] [<a href="https://github.com/a5507203/IDLN">CODE</a>]<br>
Y. Yao, T. Liu, <b>M. Gong</b>, B. Han, G. Niu, and K. Zhang.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2021.
</p></li>
<li><p>
Unaligned Image-to-Image Translation by Learning to Reweight. [<A HREF="https://arxiv.org/abs/2109.11736">PDF</A>] [<a href="https://github.com/Mid-Push/IrwGAN">CODE</a>]<br>
S. Xie, <b>M. Gong</b>, Y. Xu, and K. Zhang.<br>
In <a href="http://iccv2021.thecvf.com/"> ICCV</a>, 2021.
</p></li>
<li><p>
Domain Adaptation As a Problem of Inference on Graphical Models. [<A HREF="https://arxiv.org/abs/2002.03278">PDF</A>][<A HREF="https://github.com/mgong2/DA_Infer">CODE</A>]<br>
K. Zhang*, <b>M. Gong*</b>, P. Stojanov, B. Huang, Qingsong Liu, and C. Glymour.<br>
In <a href="https://nips.cc/">NeurIPS</a></i>, 2020.
</p></li>
<li><p>
Domain Generalization via Entropy Regularization. [<A HREF="https://papers.nips.cc/paper/2020/file/b98249b38337c5088bbc660d8f872d6a-Paper.pdf">PDF</A>][<A HREF="https://github.com/sshan-zhao/DG_via_ER">CODE</A>]<br>
S. Zhao, <b>M. Gong</b>, T. Liu, H. Fu, and D. Tao.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020.
</p></li>
<li><p>
Label-Noise Robust Domain Adaptation. [<A HREF="https://proceedings.icml.cc/static/paper_files/icml/2020/1942-Paper.pdf">PDF</A>]<br>
X. Yu, T. Liu, <b>M. Gong</b>, K. Zhang, K. Batmanghelich, and D. Tao.<br>
In <a href="https://icml.cc/"> ICML</a>, 2020.
</p></li>
<li><p>
LTF: A Label Transformation Framework for Correcting Target Shift. [<A HREF="https://proceedings.icml.cc/static/paper_files/icml/2020/1262-Paper.pdf">PDF</A>][<A HREF="https://github.com/CR-Gjx/LTF-Label-Transformation-Framework">CODE</A>] <br>
J. Guo, <b>M. Gong</b>, T. Liu, K. Zhang, and D. Tao.<br>
In <a href="https://icml.cc/"> ICML</a>, 2020.
</p></li>
<li><p>
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. [<A HREF="http://proceedings.mlr.press/v89/stojanov19a/stojanov19a.pdf">PDF</A>]<br>
P. Stojanov, <b>M. Gong</b>, J. G. Carbonell, and K. Zhang.<br>
In <a href="https://www.aistats.org/aistats2019/">AISTATS</a></i>, 2019. <br>
</p></li>
<li><p>
Data-Driven Approach to Multiple-Source Domain Adaptation. [<A HREF="http://proceedings.mlr.press/v89/stojanov19b/stojanov19b.pdf">PDF</A>]<br>
P. Stojanov, <b>M. Gong</b>, J. G. Carbonell, and K. Zhang.<br>
In <a href="https://www.aistats.org/aistats2019/">AISTATS</a></i>, 2019. <br>
</p></li>
<li><p>
Deep Domain Generalization via Conditional Invariant Adversarial Networks. [<A HREF="http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf">PDF</A>][<A HREF="http://staff.ustc.edu.cn/~xinmei/publications_pdf/2018/code-YaLi.zip">CODE</A>] <br>
Y. Li, X. Tian, <b>M. Gong</b>, Y. Liu, T. Liu, K. Zhang, and D. Tao. <br>
In <a href="https://eccv2018.org/"> ECCV</a>, 2018.
</p></li>
<li><p>
Domain Generalization via Conditional Invariant Representations. [<A HREF="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16595/16558">PDF</A>][<A HREF="papers/CIDG.zip">CODE</A>]<br>
Y. Li, <b> M. Gong</b>, X. Tian, T. Liu, and D. Tao. <br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai18.php"> AAAI</a>, 2018 (<font color="red">Oral, acceptance rate 11.0%</font>)
</p></li>
<li><p>
Domain Adaptation with Conditional Transferable Components. [<A HREF="papers/ICML_CTC.pdf">PDF</A>][<A HREF="papers/CTC.zip">CODE</A>]<br>
<b>M. Gong</b>, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.<br>
In <a href="https://icml.cc/2016/index.html">ICML</a></i>, 2016.
</p></li>
<li><p>
Multi-Source Domain Adaptation: A Causal View. [<A HREF="papers/AAAI_MULTI.pdf">PDF</A>][<A HREF="papers/MDAC.zip">CODE</A>]<br>
K. Zhang, <b>M. Gong</b>, and B. Schölkopf.<br>
In <a href="https://www.aaai.org/Press/Proceedings/aaai15.php">AAAI</a></i>, 2015.
</p></li>
</ul>
<h2><hr><a name="cd"></a>Generative Models</h2>
<ul>
<li><p>
HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback. [<A HREF="https://arxiv.org/pdf/2312.12227.pdf">PDF</A>]<br>
G. Han, S. Huang, <b>M. Gong</b>, and J. Tang.<br>
In <a href="https://aaai.org/aaai-conference/"> AAAI</a>, 2024.
</p></li>
<li><p>
Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness. [<A HREF="https://arxiv.org/pdf/2401.03476.pdf">PDF</A>]<br>
S. Yang, Z. Xu, H. Xue, Y. Cheng, S. Huang, <b>M. Gong</b>, and Z. Wu.<br>
In <a href="https://2024.ieeeicassp.org/"> ICASSP</a>, 2024.
</p></li>
<li><p>
Semi-Implicit Denoising Diffusion Models (SIDDMs). [<A HREF="https://arxiv.org/abs/2306.12511">PDF</A>]<br>
Y. Xu, <b>M. Gong</b>, S. Xie, W. Wei, M. Grundmann, K. Batmanghelich*, T. Hou*.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023. <br>
<font color="red">High-quality image generation in a few diffusion steps, an extension to text-to-image generation is <a target="_blank" href="https://arxiv.org/abs/2311.09257">here</a>.</font>
</p></li>
<li><p>
Unpaired Image-to-Image Translation with Shortest Path Regularization. [<A HREF="https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.pdf">PDF</A>]<br>
S. Xie, Y. Xu, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://cvpr2023.thecvf.com/"> CVPR</a>, 2023.
</p></li>
<li><p>
Multi-Domain Image Generation and Translation with Identifiability Guarantees. [<A HREF="https://openreview.net/forum?id=U2g8OGONA_V">PDF</A>][<A HREF="https://github.com/Mid-Push/i-stylegan">CODE</A>]<br>
S. Xie, L. Kong, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.pdf">PDF</A>] [<A href="https://github.com/CR-Gjx/SCC">CODE</A>] <br>
J. Guo, J. Li, H. Fu, <b>M. Gong</b>, K. Zhang, D. Tao.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
<li><p>
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. [<A href="https://arxiv.org/abs/2203.12707">PDF</A>] [<A href="https://github.com/batmanlab/MSPC">CODE</A>] <br>
Y. Xu, S. Xie, W. Wu, K. Zhang, <b>M. Gong*</b>, K. Batmanghelich*.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
<li><p>
Few-Shot Font Generation by Learning Fine-Grained Local Styles. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.pdf" >PDF</A>] <br>
L. Tang, Y. Cai, J. Liu, Z. Hong, <b>M. Gong</b>, M. Fan, J. Han, J. L, E. Ding, J. Wang.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
<li><p>
Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis. [<A HREF="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9770375">PDF</A>][<A HREF="https://github.com/batmanlab/HA-GAN">CODE</A>]<br>
L. Sun, J. Chen, Y. Xu, <b>M. Gong</b>, K. Yu, K. Batmanghelich<br>
<i><a target="_blank" href="https://www.embs.org/jbhi/">IEEE JBHI,</a></i> (2022).
</p></li>
<li><p>
Unaligned Image-to-Image Translation by Learning to Reweight. [<A HREF="https://arxiv.org/abs/2109.11736">PDF</A>] [<a href="https://github.com/Mid-Push/IrwGAN">CODE</a>]<br>
S. Xie, <b>M. Gong</b>, Y. Xu, and K. Zhang.<br>
In <a href="http://iccv2021.thecvf.com/"> ICCV</a>, 2021.
</p></li>
<li><p>
Twin Auxiliary Classifiers GAN. [<A HREF="https://arxiv.org/abs/1907.02690">PDF</A>][<A HREF="https://github.com/batmanlab/twin_ac">CODE</A>]<br>
<b>M. Gong*</b>, Y. Xu*, C. Li, K. Zhang, and K. Batmanghelich.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019. (<font color="red">Spotlight, acceptance rate 2.4%</font>)
</p></li>
<li><p>
Geometry-Consistent Adversarial Networks for Unsupervised Domain Mapping. [<A HREF="https://arxiv.org/abs/1809.05852">PDF</A>][<A HREF="https://github.com/hufu6371/GcGAN">CODE</A>]<br>
H. Fu*, <b>M. Gong*</b>, C. Wang, K. Batmanghelich, K. Zhang, and D. Tao.<br>
In <a href="http://cvpr2019.thecvf.com/">CVPR</a></i>, 2019. (<font color="red">best paper finalist, top 1%</font>)<br>
</p></li>
</ul>
<h2><hr><a name="cd"></a>3D Vision</h2>
<ul>
<li><p>
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. [<A HREF="">PDF</A>]<br>
L. Duan, S. Zhao, N. Xue, <b>M. Gong</b>, G. Xia, D. Tao.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering. [<A HREF="https://arxiv.org/abs/2304.10075">PDF</A>]<br>
D. Hu, Z. Zhang, T. Hou, T. Liu, H. Fu*, and <b>M. Gong*</b>.<br>
In <a href="https://iccv2023.thecvf.com/"> ICCV</a>, 2023.
</p></li>
<li><p>
Deep Corner. [<A HREF="https://link.springer.com/article/10.1007/s11263-023-01837-3">PDF</A>]<br>
S. Zhao, <b>M. Gong</b>, H. Zhao, J. Zhang and D. Tao.<br>
<i><a target="_blank" href="https://www.springer.com/journal/11263">IJCV,</a></i> (2023).
</p></li>
<li><p>
Knowledge Distillation for Feature Extraction in Underwater VSLAM. [<A HREF="https://arxiv.org/abs/2303.17981">PDF</A>] <br>
J. Yang, <b>M. Gong.</b>, G. Nair, J.H. Lee, J. Monty, Y. Pu<br>
In <a href="https://www.icra2023.org/"> ICRA</a>, 2023.
</p></li>
<li><p>
Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression. [<A HREF="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136620229.pdf">PDF</A>] <br>
D. Hu, L. Peng, T. Chu, X. Zhang, Y. Mao, H. Bondell, and <b>M. Gong.</b><br>
In <a href="https://eccv2022.ecva.net/"> ECCV</a>, 2022.
</p></li>
<li><p>
Digging into Radiance Grid for Real-Time View Synthesis with Detail Preservation. [<A HREF="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136750722.pdf">PDF</A>] <br>
J. Zhang*, J. Huang*, B. Cai*, <b>M. Gong</b>, C. Wang, B. Cai, J. Wang, H. Luo, R. Jia, B. Zhao, X. Tang, H. Fu<br>
In <a href="https://eccv2022.ecva.net/"> ECCV</a>, 2022.
</p></li>
<li><p>
A Unified B-Spline Framework for Scale-Invariant Keypoint Detection. [<A HREF="https://link.springer.com/article/10.1007/s11263-021-01568-3">PDF</A>][<A HREF="https://github.com/qizhust/UBsplines">CODE</A>] <br>
Q. Zheng, <b>M. Gong</b>, X. You, and D. Tao.<br>
<i><a target="_blank" href="https://www.springer.com/journal/11263">IJCV,</a></i> (2022).
</p></li>
<li><p>
3D-FUTURE: 3D Furniture shape with TextURE. [<A HREF="https://arxiv.org/abs/2009.09633">PDF</A>]<br>
H. Fu, R. Jia, L. Gao, <b>M. Gong</b>, B. Zhao, S. Maybank, and D. Tao.<br>
<i><a target="_blank" href="https://www.springer.com/journal/11263">IJCV,</a></i> 129(12), 3313-3337 (2021).
</p></li>
<li><p>
Adaptive Context-Aware Multi-Modal Network for Depth Completion. [<A HREF="https://arxiv.org/abs/2008.10833">PDF</A>] <br>
S. Zhao, <b>M. Gong</b>, H. Fu, D. Tao. <br>
<i><a target="_blank" href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=83">IEEE T-IP,</a></i> 30, 5264-5276 (2021).
</p></li>
<li><p>
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning. [<A HREF="https://proceedings.neurips.cc/paper/2020/hash/a87d27f712df362cd22c7a8ef823e987-Abstract.html">PDF</A>]<br>
H. Fu*, S. Li*, R. Jia, <b>M. Gong</b>, B. Zhao, and D. Tao.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020.
</p></li>
<li><p>
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation. [<A HREF="https://arxiv.org/pdf/1904.01870">PDF</A>][<A HREF="https://github.com/sshan-zhao/GASDA">CODE</A>]<br>
S. Zhao, H. Fu, <b>M. Gong</b>, and D. Tao.<br>
In <a href="http://cvpr2019.thecvf.com/">CVPR</a></i>, 2019. <br>
</p></li>
<li><p>
Deep Ordinal Regression Network for Monocular Depth Estimation. [<A HREF="https://hal.archives-ouvertes.fr/hal-01741163/file/CVPR18_DepthEstimation.pdf">PDF</A>][<A HREF="https://github.com/hufu6371/DORN">CODE</A>]<br>
H. Fu, <b>M. Gong</b>, C. Wang, K. Batmanghelich, and D. Tao. <br>
In <a href="http://cvpr2018.thecvf.com/">CVPR</a></i>, 2018.<br>
<font color="red">This algorithm won the 1st prize in single image depth prediction competition, <a target="_blank" href="http://www.robustvision.net/leaderboard.php?benchmark=depth">Robust Vision Challenge 2018</a>.</font>
</p></li>
</ul>