Jiajun Wu

All Publications

• OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer Gao, R., Si, Z., Chang, Y., Clarke, S., Bohg, J., Li Fei-Fei, Yuan, W., Wu, J., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 10588-10598

  View details for DOI 10.1109/CVPR52688.2022.01034

  View details for Web of Science ID 000870759103065

• Learning Generative Models of 3D Structures Chaudhuri, S., Ritchie, D., Wu, J., Xu, K., Zhang, H. WILEY. 2020: 643–66

  View details for DOI 10.1111/cgf.14020

  View details for Web of Science ID 000548709600052

• Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Xue, T., Wu, J., Bouman, K. L., Freeman, W. T. 2019; 41 (9): 2236–50

  Abstract

  We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.

  View details for DOI 10.1109/TPAMI.2018.2854726

  View details for Web of Science ID 000480343900014

  View details for PubMedID 30004870

• See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion SCIENCE ROBOTICS Fazeli, N., Oller, M., Wu, J., Wu, Z., Tenenbaum, J. B., Rodriguez, A. 2019; 4 (26)

  View details for DOI 10.1126/scirobotics.aav3123

  View details for Web of Science ID 000458560100005

• 3D Interpreter Networks for Viewer-Centered Wireframe Modeling INTERNATIONAL JOURNAL OF COMPUTER VISION Wu, J., Xue, T., Lim, J. J., Tian, Y., Tenenbaum, J. B., Torralba, A., Freeman, W. T. 2018; 126 (9): 1009–26

  View details for DOI 10.1007/s11263-018-1074-6

  View details for Web of Science ID 000441553300008

• Learning to Reconstruct Shapes from Unseen Classes Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J. B., Freeman, W. T., Wu, J., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823302028

• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Wu, J., Zhang, C., Xue, T., Freeman, W. T., Tenenbaum, J. B., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016

  View details for Web of Science ID 000458973700060

• Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Wu, J., Yildirim, I., Lim, J. J., Freeman, W. T., Tenenbaum, J. B., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015

  View details for Web of Science ID 000450913101040

• Differentiable Physics Simulation of Dynamics-Augmented Neural Objects IEEE ROBOTICS AND AUTOMATION LETTERS Le Cleac'h, S., Yu, H., Guo, M., Howell, T., Gao, R., Wu, J., Manchester, Z., Schwager, M. 2023; 8 (5): 2780-2787

  View details for DOI 10.1109/LRA.2023.3257707

  View details for Web of Science ID 000964797800002

• Translating a Visual LEGO Manual to a Machine-Executable Plan Wang, R., Zhang, Y., Mao, J., Cheng, C., Wu, J., Avidan, S., Brostow, G., Cisse, M., Farinella, G. M., Hassner, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 677-694

  View details for DOI 10.1007/978-3-031-19836-6_38

  View details for Web of Science ID 000903756400038

• Video Extrapolation in Space and Time Zhang, Y., Wu, J., Avidan, S., Brostow, G., Cisse, M., Farinella, G. M., Hassner, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 313-333

  View details for DOI 10.1007/978-3-031-19787-1_18

  View details for Web of Science ID 000904102900018

• Unsupervised Segmentation in Real-World Images via Spelke Object Inference Chen, H., Venkatesh, R., Friedman, Y., Wu, J., Tenenbaum, J. B., Yamins, D. K., Bear, D. M., Avidan, S., Brostow, G., Cisse, M., Farinella, G. M., Hassner, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 719-735

  View details for DOI 10.1007/978-3-031-19818-2_41

  View details for Web of Science ID 000903735000041

• Rotationally Equivariant 3D Object Detection Yu, H., Wu, J., Yi, L., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 1446-1454

  View details for DOI 10.1109/CVPR52688.2022.00151

  View details for Web of Science ID 000867754201068

• Revisiting the "Video" in Video-Language Understanding Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Li Fei-Fei, Niebles, J., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 2907-2917

  View details for DOI 10.1109/CVPR52688.2022.00293

  View details for Web of Science ID 000867754203017

• Programmatic Concept Learning for Human Motion Description and Synthesis Kulal, S., Mao, J., Aiken, A., Wu, J., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 13833-13842

  View details for DOI 10.1109/CVPR52688.2022.01347

  View details for Web of Science ID 000870759106090

• RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks Shi, H., Xu, H., Huang, Z., Li, Y., Wu, J., Hauser, K., Shell, D., Huang, S. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2022

  View details for Web of Science ID 000827625700008

• Hierarchical Motion Understanding via Motion Programs Kulal, S., Mao, J., Aiken, A., Wu, J., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 6564-6572

  View details for DOI 10.1109/CVPR46437.2021.00650

  View details for Web of Science ID 000739917306077

• 3D Shape Generation and Completion through Point-Voxel Diffusion Zhou, L., Du, Y., Wu, J., IEEE IEEE. 2021: 5806-5815

  View details for DOI 10.1109/ICCV48922.2021.00577

  View details for Web of Science ID 000797698906004

• Learning Temporal Dynamics from Cycles in Narrated Video Epstein, D., Wu, J., Schmid, C., Sun, C., IEEE IEEE. 2021: 1460-1469

  View details for DOI 10.1109/ICCV48922.2021.00151

  View details for Web of Science ID 000797698901064

• Neural Radiance Flow for 4D View Synthesis and Video Processing Du, Y., Zhang, Y., Yu, H., Tenenbaum, J. B., Wu, J., IEEE IEEE. 2021: 14304-14314

  View details for DOI 10.1109/ICCV48922.2021.01406

  View details for Web of Science ID 000798743204050

• De-rendering the World's Revolutionary Artefacts Wu, S., Makadia, A., Wu, J., Snavely, N., Tucker, R., Kanazawa, A., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 6334-6343

  View details for DOI 10.1109/CVPR46437.2021.00627

  View details for Web of Science ID 000739917306054

• pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 5795-5805

  View details for DOI 10.1109/CVPR46437.2021.00574

  View details for Web of Science ID 000739917306001

• Repopulating Street Scenes Wang, Y., Liu, A., Tucker, R., Wu, J., Curless, B. L., Seitz, S. M., Snavely, N., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 5106-5115

  View details for DOI 10.1109/CVPR46437.2021.00507

  View details for Web of Science ID 000739917305031

• KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control Jakab, T., Tucker, R., Makadia, A., Wu, J., Snavely, N., Kanazawa, A., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 12778-12787

  View details for DOI 10.1109/CVPR46437.2021.01259

  View details for Web of Science ID 000742075002096

• Augmenting Policy Learning with Routines Discovered from a Single Demonstration Zhao, Z., Gan, C., Wu, J., Guo, X., Tenenbaum, J., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2021: 11024-11032

  View details for Web of Science ID 000681269802081

• Visual Grounding of Learned Physical Models Li, Y., Lin, T., Yi, K., Bear, D. M., Yamins, D. K., Wu, J., Tenenbaum, J. B., Torralba, A., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020

  View details for Web of Science ID 000683178506006

• DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs Wang, Y., Liu, B., Wu, J., Zhu, Y., Du, S. S., Li Fei-Fei, Tenenbaum, J. B., Bessiere, C. IJCAI-INT JOINT CONF ARTIF INTELL. 2020: 4190-4198

  View details for Web of Science ID 000764196704043

• Perspective Plane Program Induction from a Single Image Li, Y., Mao, J., Zhang, X., Freeman, W. T., Tenenbaum, J. B., Wu, J., IEEE IEEE. 2020: 4433–42

  View details for DOI 10.1109/CVPR42600.2020.00449

  View details for Web of Science ID 000620679504071

• End-to-End Optimization of Scene Layout Luo, A., Zhang, Z., Wu, J., Tenenbaum, J. B., IEEE IEEE. 2020: 3753–62

  View details for DOI 10.1109/CVPR42600.2020.00381

  View details for Web of Science ID 000620679504003

• Accurate Vision-based Manipulation through Contact Reasoning Kloss, A., Bauza, M., Wu, J., Tenenbaum, J. B., Rodriguez, A., Bohg, J., IEEE IEEE. 2020: 6738-6744

  View details for Web of Science ID 000712319504073

• Video Enhancement with Task-Oriented Flow INTERNATIONAL JOURNAL OF COMPUTER VISION Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W. T. 2019; 127 (8): 1106–25

  View details for DOI 10.1007/s11263-018-01144-2

  View details for Web of Science ID 000474559000008

• An integrative computational architecture for object-driven cortex CURRENT OPINION IN NEUROBIOLOGY Yildirim, I., Wu, J., Kanwisher, N., Tenenbaum, J. 2019; 55: 73–81

  Abstract

  Computational architecture for object-driven cortex Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architecture spanning all of object-driven cortex? Inspired by recent work in artificial intelligence (AI), machine learning, and cognitive science, we consider the hypothesis that these regions can be understood as a coherent network implementing an integrative computational system that unifies the functions needed to perceive, predict, reason about, and plan with physical objects-as in the paradigmatic case of using or making tools. Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations. We review evidence relating specific components of our proposal to the specific regions that comprise object-driven cortex, and lay out future research directions with the goal of building a complete functional and mechanistic account of this system.

  View details for DOI 10.1016/j.conb.2019.01.010

  View details for Web of Science ID 000472127600011

  View details for PubMedID 30825704

  View details for PubMedCentralID PMC6548583

• Visual Concept-Metaconcept Learning Han, C., Mao, J., Gan, C., Tenenbaum, J. B., Wu, J., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000534424305005

• Combining Physical Simulators and Object-Based Networks for Control Ajay, A., Bauza, M., Wu, J., Fazeli, N., Tenenbaum, J. B., Rodriguez, A., Kaelbling, L. P., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 3217–23

  View details for Web of Science ID 000494942302054

• Propagation Networks for Model-Based Control Under Partial Observation Li, Y., Wu, J., Zhu, J., Tenenbaum, J. B., Torralba, A., Tedrake, R., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 1205–11

  View details for Web of Science ID 000494942300127

• ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics Hu, Y., Liu, J., Spielberg, A., Tenenbaum, J. B., Freeman, W. T., Wu, J., Rus, D., Matusik, W., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 6265–71

  View details for Web of Science ID 000494942304086

• Program-Guided Image Manipulators Mao, J., Zhang, X., Li, Y., Freeman, W. T., Tenenbaum, J. B., Wu, J., IEEE IEEE COMPUTER SOC. 2019: 4029–38

  View details for DOI 10.1109/ICCV.2019.00413

  View details for Web of Science ID 000531438104018

• Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations Smith, K. A., Mei, L., Yao, S., Wu, J., Spelke, E., Tenenbaum, J. B., Ullman, T. D., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866900056

• Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning INTERNATIONAL JOURNAL OF COMPUTER VISION Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., Torralba, A. 2018; 126 (10): 1120–37

  View details for DOI 10.1007/s11263-018-1083-5

  View details for Web of Science ID 000443018400004

• Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks Zheng, D., Luo, V., Wu, J., Tenenbaum, J. B., Globerson, A., Silva, R. AUAI PRESS. 2018: 497–507

  View details for Web of Science ID 000493119200049

• Physical Primitive Decomposition Liu, Z., Freeman, W. T., Tenenbaum, J. B., Wu, J., Ferrari, Hebert, M., Sminchisescu, C., Weiss, Y. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 3–20

  View details for DOI 10.1007/978-3-030-01258-8_1

  View details for Web of Science ID 000604449400001

• Seeing Tree Structure from Vibration Xue, T., Wu, J., Zhang, Z., Zhang, C., Tenenbaum, J. B., Freeman, W. T., Ferrari, Hebert, M., Sminchisescu, C., Weiss, Y. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 762–79

  View details for DOI 10.1007/978-3-030-01240-3_46

  View details for Web of Science ID 000594233000046

• Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification Long, X., Gan, C., de Melo, G., Wu, J., Liu, X., Wen, S., IEEE IEEE. 2018: 7834–43

  View details for DOI 10.1109/CVPR.2018.00817

  View details for Web of Science ID 000457843607102

• Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J. B., Freeman, W. T., IEEE IEEE. 2018: 2974–83

  View details for DOI 10.1109/CVPR.2018.00314

  View details for Web of Science ID 000457843603012

• MoSculp: Interactive Visualization of Shape and Time Zhang, X., Dekel, T., Xue, T., Owens, A., He, Q., Wu, J., Mueller, S., Freeman, W. T., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 275–85

  View details for DOI 10.1145/3242587.3242592

  View details for Web of Science ID 000494260500025

• 3D Shape Perception from Monocular Vision, Touch, and Shape Priors Wang, S., Wu, J., Sun, X., Yuan, W., Freeman, W. T., Tenenbaum, J. B., Adelson, E. H., Kosecka, J., Maciejewski, A. A., Okamura, A., Bicchi, A., Stachniss, C., Song, D. Z., Lee, D. H., Chaumette, F., Ding, H., Li, J. S., Wen, J., Roberts, J., Masamune, K., Chong, N. Y., Amato, N., Tsagwarakis, N., Rocco, P., Asfour, T., Chung, W. K., Yasuyoshi, Y., Sun, Y., Maciekeski, T., Althoefer, K., AndradeCetto, J., Chung, W. K., Demircan, E., Dias, J., Fraisse, P., Gross, R., Harada, H., Hasegawa, Y., Hayashibe, M., Kiguchi, K., Kim, K., Kroeger, T., Li, Y., Ma, S., Mochiyama, H., Monje, C. A., Rekleitis, Roberts, R., Stulp, F., Tsai, C. H., Zollo, L. IEEE. 2018: 1606–13

  View details for Web of Science ID 000458872701105

• 3D-Aware Scene Manipulation via Inverse Graphics Yao, S., Hsu, T., Zhu, J., Wu, J., Torralba, A., Freeman, W. T., Tenenbaum, J. B., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823301084

• Visual Object Networks: Image Generation with Disentangled 3D Representation Zhu, J., Zhang, Z., Zhang, C., Wu, J., Torralba, A., Tenenbaum, J. B., Freeman, W. T., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823300012

• Learning to Exploit Stability for 3D Scene Parsing Du, Y., Liu, Z., Basevi, H., Leonardis, A., Freeman, W. T., Tenenbaum, J. B., Wu, J., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823301069

• Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., Tenenbaum, J. B., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823301006

• Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing Ajay, A., Wu, J., Fazeli, N., Bauza, M., Kaelbling, L. P., Tenenbaum, J. B., Rodriguez, A., Kosecka, J., Maciejewski, A. A., Okamura, A., Bicchi, A., Stachniss, C., Song, D. Z., Lee, D. H., Chaumette, F., Ding, H., Li, J. S., Wen, J., Roberts, J., Masamune, K., Chong, N. Y., Amato, N., Tsagwarakis, N., Rocco, P., Asfour, T., Chung, W. K., Yasuyoshi, Y., Sun, Y., Maciekeski, T., Althoefer, K., AndradeCetto, J., Chung, W. K., Demircan, E., Dias, J., Fraisse, P., Gross, R., Harada, H., Hasegawa, Y., Hayashibe, M., Kiguchi, K., Kim, K., Kroeger, T., Li, Y., Ma, S., Mochiyama, H., Monje, C. A., Rekleitis, Roberts, R., Stulp, F., Tsai, C. H., Zollo, L. IEEE. 2018: 3066–73

  View details for Web of Science ID 000458872702129

• Shape and Material from Sound Zhang, Z., Li, Q., Huang, Z., Wu, J., Tenenbaum, J. B., Freeman, W. T., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649401031

• Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks Soltani, A., Huang, H., Wu, J., Kulkarni, T. D., Tenenbaum, J. B., IEEE IEEE. 2017: 2511–19

  View details for DOI 10.1109/CVPR.2017.269

  View details for Web of Science ID 000418371402061

• Neural Scene De-rendering Wu, J., Tenenbaum, J. B., Kohli, P., IEEE IEEE. 2017: 7035–43

  View details for DOI 10.1109/CVPR.2017.744

  View details for Web of Science ID 000418371407015

• Raster-to-Vector: Revisiting Floorplan Transformation Liu, C., Wu, J., Kohli, P., Furukawa, Y., IEEE IEEE. 2017: 2214–22

  View details for DOI 10.1109/ICCV.2017.241

  View details for Web of Science ID 000425498402029

• Generative Modeling of Audible Shapes for Object Perception Zhang, Z., Wu, J., Li, Q., Huang, Z., Traer, J., McDermott, J. H., Tenenbaum, J. B., Freeman, W. T., IEEE IEEE. 2017: 1260–69

  View details for DOI 10.1109/ICCV.2017.141

  View details for Web of Science ID 000425498401034

• Self-Supervised Intrinsic Image Decomposition Janner, M., Wu, J., Kulkarni, T. D., Yildirim, I., Tenenbaum, J. B., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649406002

• MarrNet: 3D Shape Reconstruction via 2.5D Sketches Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W. T., Tenenbaum, J. B., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649400052

• Learning to See Physics via Visual De-animation Wu, J., Lu, E., Kohli, P., Freeman, W. T., Tenenbaum, J. B., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649400015

• Single Image 3D Interpreter Network Wu, J., Xue, T., Lim, J. J., Tian, Y., Tenenbaum, J. B., Torralba, A., Freeman, W. T., Leibe, B., Matas, J., Sebe, N., Welling, M. SPRINGER INT PUBLISHING AG. 2016: 365–82

  View details for DOI 10.1007/978-3-319-46466-4_22

  View details for Web of Science ID 000389499900022

• Ambient Sound Provides Supervision for Visual Learning Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., Torralba, A., Leibe, B., Matas, J., Sebe, N., Welling, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2016: 801–16

  View details for DOI 10.1007/978-3-319-46448-0_48

  View details for Web of Science ID 000389382700048

• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Xue, T., Wu, J., Bouman, K. L., Freeman, W. T., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016

  View details for Web of Science ID 000458973704076

• Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Zhu, J., Wu, J., Xu, Y., Chang, E., Tu, Z. 2015; 37 (4): 862–75

  Abstract

  In this paper, we tackle the problem of common object (multiple classes) discovery from a set of input images, where we assume the presence of one object class in each image. This problem is, loosely speaking, unsupervised since we do not know a priori about the object type, location, and scale in each image. We observe that the general task of object class discovery in a fully unsupervised manner is intrinsically ambiguous; here we adopt saliency detection to propose candidate image windows/patches to turn an unsupervised learning problem into a weakly-supervised learning problem. In the paper, we propose an algorithm for simultaneously localizing objects and discovering object classes via bottom-up (saliency-guided) multiple class learning (bMCL). Our contributions are three-fold: (1) we adopt saliency detection to convert unsupervised learning into multiple instance learning, formulated as bottom-up multiple class learning (bMCL); (2) we propose an integrated framework that simultaneously performs object localization, object class discovery, and object detector training; (3) we demonstrate that our framework yields significant improvements over existing methods for multi-class object discovery and possess evident advantages over competing methods in computer vision. In addition, although saliency detection has recently attracted much attention, its practical usage for high-level vision tasks has yet to be justified. Our method validates the usefulness of saliency detection to output "noisy input" for a top-down method to extract common patterns.

  View details for DOI 10.1109/TPAMI.2014.2353617

  View details for Web of Science ID 000351213400012

  View details for PubMedID 26353299

• Deep Multiple Instance Learning for Image Classification and Auto-Annotation Wu, J., Yu, Y., Huang, C., Yu, K., IEEE IEEE. 2015: 3460–69

  View details for Web of Science ID 000387959203053

• MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation Wu, J., Zhao, Y., Zhu, J., Luo, S., Tu, Z., IEEE IEEE. 2014: 256–63

  View details for DOI 10.1109/CVPR.2014.40

  View details for Web of Science ID 000361555600033

• Harvesting Mid-level Visual Concepts from Large-scale Internet Images Li, Q., Wu, J., Tul, Z., IEEE IEEE. 2013: 851–58

  View details for DOI 10.1109/CVPR.2013.115

  View details for Web of Science ID 000331094300108

• A classification approach to coreference in discharge summaries: 2011 i2b2 challenge JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Xu, Y., Liu, J., Wu, J., Wang, Y., Tu, Z., Sun, J., Tsujii, J., Chang, E. 2012; 19 (5): 897–905

  Abstract

  To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.

  View details for DOI 10.1136/amiajnl-2011-000734

  View details for Web of Science ID 000307934600030

  View details for PubMedID 22505762

  View details for PubMedCentralID PMC3422828

• Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning Zhu, J., Wu, J., Wei, Y., Chang, E., Tu, Z., IEEE IEEE. 2012: 3218–25

  View details for Web of Science ID 000309166203049