Tianfu (Matt) Wu

Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian

The Forty-second International Conference on Machine Learning, ICML.

DiffMesh: A Motion-Aware Diffusion Framework for Human Mesh Recovery from Videos

Zheng, C., Liu, X., Peng, Q., Wu, T., Wang, P., & Chen, C. (2025, February 26). 2025 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION, WACV, pp. 4891–4901.

Exploratory Analysis of the Regulation of Long Non-Coding RNA Transcription with Nucleotide Large Language Models

Wang, W., Hou, Z., Wu, T., Liu, X., & Peng, X. (2025, December 15). (Vol. 12). Vol. 12.

GSBA$\textasciicircumK$: $top$-$K$ Geometric Score-based Black-box Attack

The Thirteenth International Conference on Learning Representations. https://openreview.net/forum?id=htX7AoHyln

H$^3$GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning

https://arxiv.org/abs/2504.11699

Hierarchical Ensemble Based Clustering For Networked Microgrids

Joshi, A., Wu, T., & Chow, M.-Y. (2025, October 14).

ScaleLSD: Scalable Deep Line Segment Detection Streamlined

IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR.

WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models

The Forty-second International Conference on Machine Learning, ICML.

A Pixel Is Worth More Than One 3D Gaussians in Single-View 3D Reconstruction

Shen, J., Xue, N., & Wu, T. (2024, May 30). ArXiv (Cornell University).

Enhancing Approximate Message Passing via Diffusion Models Towards On-Device Intelligence

Dai, P., Yue, K., Jin, R., Wu, T. M., & Xiong, K. (2024, June 9). 2024 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS, ICC WORKSHOPS 2024, pp. 890–895.

High-Speed Receiver Transient Modeling with Generative Adversarial Networks

Multi-View Attentive Contextualization for Multi-View 3D Object Detection

Liu, X., Zheng, C., Qian, M., Xue, N., Chen, C., Zhang, Z., … Wu, T. (2024, June 16). 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 16688–16698.

NEAT: Distilling 3D Wireframes from Neural Attraction Fields

Xue, N., Tan, B., Xiao, Y., Dong, L., Xia, G.-S., Wu, T., & Shen, Y. (2024, June 16). 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 19968–19977.

Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience

Jin, R., Liu, Y., Huang, Y., He, X., Wu, T., & Dai, H. (2024, January 12). IEEE Transactions on Neural Networks and Learning Systems, Vol. 1.

CGBA: Curvature-aware Geometric Black-box Attack

Reza, M. F., Rahmati, A., Wu, T., & Dai, H. (2023, October 1).

CGBA: Curvature-aware Geometric Black-box Attack

Proceedings of the IEEE/CVF International Conference on Computer Vision, 124–133.

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Xue, N., Wu, T., Bai, S., Wang, F.-D., Xia, G.-S., Zhang, L., & Torr, P. H. S. (2023, September 7). IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, pp. 1–17.

Learning Spatially-Adaptive Squeeze-Excitation Networks for Few Shot Image Synthesis

Shen, J., & Wu, T. (2023, September 11). 2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 2855–2859.

Learning Spatially-Adaptive Style-Modulation Networks for Single Image Synthesis

Shen, J., & Wu, T. (2023, September 11). 2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 1455–1459.

Level-S²fM: Structure from Motion on Neural Level Set of Implicit Surfaces

Xiao, Y., Xue, N., Wu, T., & Xia, G.-S. (2023, June 1). 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17205–17214.

Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver

Proceedings of the IEEE/CVF International Conference on Computer Vision, 6436–6446.

NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

Tan, B., Xue, N., Wu, T., & Xia, G.-S. (2023, September 12). IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, pp. 1–15.

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers

Grainger, R., Paniagua, T., Song, Xi, Cuntoor, N., Lee, M. W., & Wu, T. (2023, June 1). 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

QuadAttacK: A Quadratic Programming Approach to Learning Ordered Top-K Adversarial Attacks

Advances in Neural Information Processing Systems.

Thermal Estimation for 3D-ICs Through Generative Networks

Volumetric Wireframe Parsing from Neural Attraction Fields

HoW-3D: Holistic 3D Wireframe Perception from a Single Image

Ma, W., Tan, B., Xue, N., Wu, T., Zheng, X., & Xia, G.-S. (2022, September 1). 2022 INTERNATIONAL CONFERENCE ON 3D VISION, 3DV, pp. 596–605.

Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

Xue, N., Wu, T., Xia, G.-S., & Zhang, L. (2022, June 1). 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13055–13064.

Learning auxiliary monocular contexts helps monocular 3D object detection

Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1810–1818.

Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs

Foster, M., Wu, T., Roberts, D. L., & Bozkurt, A. (2022, October 8). Sensors, Vol. 10.

Refining Self-Supervised Learning in Imaging: Beyond Linear Metric

Jiang, B., Krim, H., Wu, T., & Cansever, D. (2022, October 16). 2022 IEEE International Conference on Image Processing (ICIP), pp. 76–80.

Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching

Advances in Neural Information Processing Systems, 35, 16305–16318.

RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks

Kashyap, P., Gajjar, A., Choi, Y., Wong, C.-W., Baron, D., Wu, T., … Franzon, P. (2022, September 6). MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD).

Event driven sensor fusion

Roheda, S., Krim, H., Luo, Z.-Q., & Wu, T. (2021, July 2). Signal Processing, Vol. 11.

High Speed Receiver Modeling Using Generative Adversarial Networks

Kashyap, P., Pitts, W. S., Baron, D., Wong, C.-W., Wu, T., & Franzon, P. D. (2021, October 17). IEEE 30TH CONFERENCE ON ELECTRICAL PERFORMANCE OF ELECTRONIC PACKAGING AND SYSTEMS (EPEPS 2021).

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis

Sun, W., & Wu, T. (2021, January 1). IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1.

Local Clustering with Mean Teacher for Semi-supervised learning

Chen, Z., Dutton, B., Ramachandra, B., Wu, T., & Vatsavai, R. R. (2021, January 10). 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR).

PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

Tan, B., Xue, N., Bai, S., Wu, T., & Xia, G.-S. (2021, October 1). 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Quantitative Study on Error Sensitivity in Ultrasound Probe Calibration with Hybrid Tracking

A Bottom-Up and Top-Down Integration Framework for Online Object Tracking

Li, M., Peng, L., Wu, T., & Peng, Z. (2020, March 6). IEEE Transactions on Multimedia, Vol. 23, pp. 105–119.

Holistically-Attracted Wireframe Parsing

Xue, N., Wu, T., Bai, S., Wang, F., Xia, G.-S., Zhang, L., & Torr, P. H. S. (2020, June 1). 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp. 2785–2794.

AOGNets: Compositional Grammatical Architectures for Deep Learning

Li, X., Song, & Wu, T. (2019, June 1). 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), pp. 6213–6223.

Image Synthesis From Reconfigurable Layout and Style

Sun, W., & Wu, T. (2019, October 1). 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), pp. 10530–10539.

Jointly social grouping and identification in visual dynamics with causality-induced hierarchical Bayesian model

Xie, Z., Wu, T., Yang, X., Zhang, L., & Wu, K. (2019, January 4). Journal of Visual Communication and Image Representation, Vol. 59, pp. 62–75.

Learning Attraction Field Representation for Robust Line Segment Detection

Xue, N., Bai, S., Wang, F., Xia, G.-S., Wu, T., & Zhang, L. (2019, June 1). 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), pp. 1595–1603.

Learning Regional Attraction for Line Segment Detection

Xue, N., Bai, S., Wang, F.-D., Xia, G.-S., Wu, T., Zhang, L., & Torr, P. H. S. (2019, December 9). IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6.

Neural Abstract Style Transfer for Chinese Traditional Painting

Li, B., Xiong, C., Wu, T., Zhou, Y., Zhang, L., & Chu, R. (2019, January 1). Lecture Notes in Computer Science, Vol. 11362, pp. 212–227.

Towards Interpretable Object Detection by Unfolding Latent Structures

Wu, T., & Song. (2019, October 1). 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), pp. 6032–6042.

Vision-based integrated mobile robotic system for real-time applications in construction

Automation in Construction, 96, 470–482.

Zero-Shot Learning Posed as a Missing Data Problem

Zhao, B., Wu, B., Wu, T., & Wang, Y. (2017, October 1). 2017 Ieee International Conference on Computer Vision Workshops (Iccvw 2017), pp. 2616–2622.

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

Li, Y., Sun, B., Wu, T., & Wang, Y. (2016, January 1). Lecture Notes in Computer Science, Vol. 9907, pp. 420–436.

Online Object Tracking, Learning and Parsing with And-Or Graphs

Wu, T., Lu, Y., & Zhu, S.-C. (2016, December 24). IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, pp. 2465–2480.

A Reconfigurable Tangram Model for Scene Representation and Categorization

IEEE Transactions on Image Processing, 25(1), 150–166.

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.

A global energy optimization framework for 2.1D sketch extraction from monocular images

Graphical Models, 76(5), 507–521.

Coupling-and-decoupling: A hierarchical model for occlusion-free object detection

Pattern Recognition, 47(10), 3254–3264.

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).

Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(5), 1013–1027.

Learning mixtures of Bernoulli templates by two-round EM with performance guarantee

Electronic Journal of Statistics, 8(2), 3004–3030.

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).

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).

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.

A stochastic graph grammar for compositional object representation and recognition

Pattern Recognition, 42(7), 1297–1307.