Tianfu Wu

Jin, R., Liu, Y., Huang, Y., He, X., Wu, T., & Dai, H. (2024, January 12). Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol. 1. https://doi.org/10.1109/TNNLS.2023.3345367

Reza, M. F., Rahmati, A., Wu, T., & Dai, H. (2023). CGBA: Curvature-aware Geometric Black-box Attack. 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, pp. 124–133. https://doi.org/10.1109/ICCV51070.2023.00018

Reza, M. F., Rahmati, A., Wu, T., & Dai, H. (2023). CGBA: Curvature-aware Geometric Black-box Attack. Proceedings of the IEEE/CVF International Conference on Computer Vision, 124–133.

Xue, N., Wu, T., Bai, S., Wang, F.-D., Xia, G.-S., Zhang, L., & Torr, P. H. S. (2023). Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(12), 14727–14744. https://doi.org/10.1109/tpami.2023.3312749

Shen, J., & Wu, T. (2023). LEARNING SPATIALLY-ADAPTIVE SQUEEZE-EXCITATION NETWORKS FOR FEW SHOT IMAGE SYNTHESIS. 2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 2855–2859. https://doi.org/10.1109/ICIP49359.2023.10222248

Shen, J., & Wu, T. (2023). LEARNING SPATIALLY-ADAPTIVE STYLE-MODULATION NETWORKS FOR SINGLE IMAGE SYNTHESIS. 2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 1455–1459. https://doi.org/10.1109/ICIP49359.2023.10222483

Xiao, Y., Xue, N., Wu, T., & Xia, G.-S. (2023). Level-S<SUP>2</SUP>fM: Structure from Motion on Neural Level Set of Implicit Surfaces. 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 17205–17214. https://doi.org/10.1109/cvpr52729.2023.01650

Liu, X., Zheng, C., Cheng, K. B., Xue, N., Qi, G.-J., & Wu, T. (2023). Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver. Proceedings of the IEEE/CVF International Conference on Computer Vision, 6436–6446.

Tan, B., Xue, N., Wu, T., & Xia, G.-S. (2023). NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(12), 15233–15248. https://doi.org/10.1109/tpami.2023.3314745

Grainger, R., Paniagua, T., Song, Xi, Cuntoor, N., Lee, M. W., & Wu, T. (2023). PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.01781

Paniagua, T., Grainger, R., & Wu, T. (2023). QuadAttacK: A Quadratic Programming Approach to Learning Ordered Top-K Adversarial Attacks. Advances in Neural Information Processing Systems.

Kashyap, P., Ravichandiran, P. P., Wang, L., Baron, D., Wong, C.-W., Wu, T., & Franzon, P. D. (2023). Thermal Estimation for 3D-ICs through Generative Networks. 2023 IEEE INTERNATIONAL 3D SYSTEMS INTEGRATION CONFERENCE, 3DIC. https://doi.org/10.1109/3DIC57175.2023.10154977

Xue, N., Tan, B., Xiao, Y., Dong, L., Xia, G.-S., & Wu, T. (2023). Volumetric Wireframe Parsing from Neural Attraction Fields.

Ma, W., Tan, B., Xue, N., Wu, T., Zheng, X., & Xia, G.-S. (2022). HoW-3D: Holistic 3DWireframe Perception from a Single Image. 2022 INTERNATIONAL CONFERENCE ON 3D VISION, 3DV, pp. 596–605. https://doi.org/10.1109/3DV57658.2022.00070

Sun, W., & Wu, T. (2022). Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 44(9), 5070–5087. https://doi.org/10.1109/TPAMI.2021.3078577

Xue, N., Wu, T., Xia, G.-S., & Zhang, L. (2022). Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation. 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 13055–13064. https://doi.org/10.1109/CVPR52688.2022.01272

Liu, X., Xue, N., & Wu, T. (2022). Learning auxiliary monocular contexts helps monocular 3D object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1810–1818.

Kashyap, P., Choi, Y., Dey, S., Baron, D., Wong, C.-W., Wu, T., … Franzon, P. D. (2022). Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks. IEEE 72ND ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2022), pp. 1958–1963. https://doi.org/10.1109/ECTC51906.2022.00307

Foster, M., Wu, T., Roberts, D. L., & Bozkurt, A. (2022). Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs. SENSORS, 22(19). https://doi.org/10.3390/s22197631

Jiang, B., Krim, H., Wu, T., & Cansever, D. (2022). REFINING SELF-SUPERVISED LEARNING IN IMAGING: BEYOND LINEAR METRIC. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 76–80. https://doi.org/10.1109/ICIP46576.2022.9897745

Cheng, K., Wu, T., & Healey, C. (2022). Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching. Advances in Neural Information Processing Systems, 35, 16305–16318.

Kashyap, P., Gajjar, A., Choi, Y., Wong, C.-W., Baron, D., Wu, T., … Franzon, P. (2022). RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks. MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), pp. 167–172. https://doi.org/10.1145/3551901.3556480

Li, M., Peng, L., Wu, T., & Peng, Z. (2021). A Bottom-Up and Top-Down Integration Framework for Online Object Tracking. IEEE TRANSACTIONS ON MULTIMEDIA, 23, 105–119. https://doi.org/10.1109/TMM.2020.2978623

Roheda, S., Krim, H., Luo, Z.-Q., & Wu, T. (2021). Event driven sensor fusion. SIGNAL PROCESSING, 188. https://doi.org/10.1016/j.sigpro.2021.108241

Kashyap, P., Pitts, W. S., Baron, D., Wong, C.-W., Wu, T., & Franzon, P. D. (2021). High Speed Receiver Modeling Using Generative Adversarial Networks. IEEE 30TH CONFERENCE ON ELECTRICAL PERFORMANCE OF ELECTRONIC PACKAGING AND SYSTEMS (EPEPS 2021). https://doi.org/10.1109/EPEPS51341.2021.9609124

Xue, N., Bai, S., Wang, F.-D., Xia, G.-S., Wu, T., Zhang, L., & Torr, P. H. S. (2021). Learning Regional Attraction for Line Segment Detection. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 43(6), 1998–2013. https://doi.org/10.1109/TPAMI.2019.2958642

Chen, Z., Dutton, B., Ramachandra, B., Wu, T., & Vatsavai, R. R. (2021). Local Clustering with Mean Teacher for Semi-supervised learning. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp. 6243–6250. https://doi.org/10.1109/ICPR48806.2021.9412469

Tan, B., Xue, N., Bai, S., Wu, T., & Xia, G.-S. (2021). PlaneTR: Structure-Guided Transformers for 3D Plane Recovery. 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), pp. 4166–4175. https://doi.org/10.1109/ICCV48922.2021.00415

Cai, Q., Wu, T., Lu, J.-yu, Prieto, J. C., Rosenbaum, A. J., Stringer, J. S. A., & Jiang, X. (2021). Quantitative Study on Error Sensitivity in Ultrasound Probe Calibration with Hybrid Tracking. INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021). https://doi.org/10.1109/IUS52206.2021.9593708

Xue, N., Wu, T., Bai, S., Wang, F., Xia, G.-S., Zhang, L., & Torr, P. H. S. (2020). Holistically-Attracted Wireframe Parsing. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 2785–2794. https://doi.org/10.1109/CVPR42600.2020.00286

Li, X., Song, Xi, & Wu, T. (2019). AOGNets: Compositional Grammatical Architectures for Deep Learning. 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), pp. 6213–6223. https://doi.org/10.1109/CVPR.2019.00638

Sun, W., & Wu, T. (2019). Image Synthesis From Reconfigurable Layout and Style. 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), pp. 10530–10539. https://doi.org/10.1109/ICCV.2019.01063

Xie, Z., Wu, T., Yang, X., Zhang, L., & Wu, K. (2019). Jointly social grouping and identification in visual dynamics with causality-induced hierarchical Bayesian model. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 59, 62–75. https://doi.org/10.1016/j.jvcir.2019.01.006

Xue, N., Bai, S., Wang, F., Xia, G.-S., Wu, T., & Zhang, L. (2019). Learning Attraction Field Representation for Robust Line Segment Detection. 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), pp. 1595–1603. https://doi.org/10.1109/CVPR.2019.00169

Li, B., Xiong, C., Wu, T., Zhou, Y., Zhang, L., & Chu, R. (2019). Neural Abstract Style Transfer for Chinese Traditional Painting. COMPUTER VISION - ACCV 2018, PT II, Vol. 11362, pp. 212–227. https://doi.org/10.1007/978-3-030-20890-5_14

Wu, T., & Song, Xi. (2019). Towards Interpretable Object Detection by Unfolding Latent Structures. 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), pp. 6032–6042. https://doi.org/10.1109/ICCV.2019.00613

Asadi, K., Ramshankar, H., Pullagurla, H., Bhandare, A., Shanbhag, S., Mehta, P., … Wu, T. (2018). Vision-based integrated mobile robotic system for real-time applications in construction. Automation in Construction, 96, 470–482. https://doi.org/10.1016/J.AUTCON.2018.10.009

Wu, T., Lu, Y., & Zhu, S.-C. (2017). Online Object Tracking, Learning and Parsing with And-Or Graphs. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 39(12), 2465–2480. https://doi.org/10.1109/tpami.2016.2644963

Zhao, B., Wu, B., Wu, T., & Wang, Y. (2017). Zero-Shot Learning posed as a Missing Data Problem. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017), pp. 2616–2622. https://doi.org/10.1109/iccvw.2017.310

Zhu, J., Wu, T., Zhu, S.-C., Yang, X., & Zhang, W. (2016). A Reconfigurable Tangram Model for Scene Representation and Categorization. IEEE Transactions on Image Processing, 25(1), 150–166. https://doi.org/10.1109/tip.2015.2498407

Li, Y., Sun, B., Wu, T., & Wang, Y. (2016). Face Detection with End-to-End Integration of a ConvNet and a 3D Model. COMPUTER VISION - ECCV 2016, PT III, Vol. 9907, pp. 420–436. https://doi.org/10.1007/978-3-319-46487-9_26

Wu, T., Li, B., & Zhu, S.-C. (2016). Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1829–1843. https://doi.org/10.1109/tpami.2015.2497699

Wu, T., & Zhu, S.-C. (2015). Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(5), 1013–1027. https://doi.org/10.1109/tpami.2014.2359653

Yu, C.-C., Liu, Y.-J., Wu, M. T., Li, K.-Y., & Fu, X. (2014). A global energy optimization framework for 2.1D sketch extraction from monocular images. Graphical Models, 76(5), 507–521. https://doi.org/10.1016/j.gmod.2014.03.015

Li, B., Song, X., Wu, T., Hu, W., & Pei, M. (2014). Coupling-and-decoupling: A hierarchical model for occlusion-free object detection. Pattern Recognition, 47(10), 3254–3264. https://doi.org/10.1016/j.patcog.2014.04.016

Li, B., Wu, T., & Zhu, S.-C. (2014). Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision – ECCV 2014 (pp. 652–667). https://doi.org/10.1007/978-3-319-10599-4_42

Barbu, A., Wu, T., & Wu, Y. N. (2014). Learning mixtures of Bernoulli templates by two-round EM with performance guarantee. Electronic Journal of Statistics, 8(2), 3004–3030. https://doi.org/10.1214/14-ejs981

Li, B., Wu, T., Hu, W., & Pei, M. (2013). Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detection. In K. M. Lee, Y. Matsushita, J. M. Rehg, & Z. Hu (Eds.), Computer Vision – ACCV 2012 (pp. 164–175). https://doi.org/10.1007/978-3-642-37331-2_13

Xie, Y., Pei, M., Liu, Z., & Wu, T. (2013). Tracking Pedestrian with Multi-component Online Deformable Part-Based Model. In K. M. Lee, Y. Matsushita, J. M. Rehg, & Z. Hu (Eds.), Computer Vision – ACCV 2012 (pp. 664–676). https://doi.org/10.1007/978-3-642-37431-9_51

Wu, T., & Zhu, S.-C. (2010). A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs. International Journal of Computer Vision, 93(2), 226–252. https://doi.org/10.1007/s11263-010-0346-6

Lin, L., Wu, T., Porway, J., & Xu, Z. (2009). A stochastic graph grammar for compositional object representation and recognition. Pattern Recognition, 42(7), 1297–1307. https://doi.org/10.1016/j.patcog.2008.10.033