Sort by:

• Highly Influential Citations
• Citations
• Acceleration
• Velocity
• Recency

Learn More

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

• Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum
• NIPS
• 2016

We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging… (More)

Single Image 3D Interpreter Network

• Jiajun Wu, Tianfan Xue, +4 authors William T. Freeman
• ECCV
• 2016

Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this… (More)

Deep multiple instance learning for image classification and auto-annotation

• Jiajun Wu, Yinan Yu, Chang Huang, Kai Yu
• 2015 IEEE Conference on Computer Vision and…
• 2015

The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection. However, there has been little… (More)

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

• Tianfan Xue, Jiajun Wu, Katherine L. Bouman, William T. Freeman
• NIPS
• 2016

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or… (More)

MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation

• Jiajun Wu, Yibiao Zhao, Jun-Yan Zhu, Siwei Luo, Zhuowen Tu
• 2014 IEEE Conference on Computer Vision and…
• 2014

Interactive segmentation, in which a user provides a bounding box to an object of interest for image segmentation, has been applied to a variety of applications in image editing, crowdsourcing,… (More)

Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

• Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Joshua B. Tenenbaum
• NIPS
• 2015

Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images. We propose a generative model for solving… (More)

Harvesting Mid-level Visual Concepts from Large-Scale Internet Images

• Quannan Li, Jiajun Wu, Zhuowen Tu
• 2013 IEEE Conference on Computer Vision and…
• 2013

Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a… (More)

Ambient Sound Provides Supervision for Visual Learning

• Andrew Owens, Jiajun Wu, Josh H. McDermott, William T. Freeman, Antonio Torralba
• ECCV
• 2016

Sound (cluster) 36.7 45.8 44.8 44.3 17.3 22.9 20.7 14.9 Sound (binary) 39.4 46.7 47.1 47.4 17.1 22.5 21.3 21.4 Sound (spectrum only) 35.8 44.0 44.4 44.4 14.6 19.5 18.6 17.7 Texton-CNN 28.9 37.5 35.3… (More)

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

• Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, William T. Freeman, Joshua B. Tenenbaum
• NIPS
• 2017

3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-based approaches,… (More)

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

• Xingyuan Sun, Jiajun Wu, +5 authors William T. Freeman
• 2018 IEEE/CVF Conference on Computer Vision and…
• 2018

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D… (More)

• ‹
• 1
• 2
• 3
• 4
• 5
• ›