Search Results for author: Boyang Deng

Found 14 papers, 5 papers with code

NASA Neural Articulated Shape Approximation

no code implementations ECCV 2020 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

Revisiting 3D Object Detection From an Egocentric Perspective

no code implementations NeurIPS 2021 Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov

Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours.

3D Object Detection Autonomous Driving

NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination

1 code implementation3 Jun 2021 Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng, Paul Debevec, William T. Freeman, Jonathan T. Barron

This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.

Offboard 3D Object Detection from Point Cloud Sequences

no code implementations CVPR 2021 Charles R. Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, Dragomir Anguelov

While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.

3D Object Detection 3D Object Recognition +1

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

no code implementations CVPR 2021 Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions.

NASA: Neural Articulated Shape Approximation

no code implementations6 Dec 2019 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

Cerberus: A Multi-headed Derenderer

no code implementations28 May 2019 Boyang Deng, Simon Kornblith, Geoffrey Hinton

To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose.

BlockQNN: Efficient Block-wise Neural Network Architecture Generation

2 code implementations16 Aug 2018 Zhao Zhong, Zichen Yang, Boyang Deng, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu

The block-wise generation brings unique advantages: (1) it yields state-of-the-art results in comparison to the hand-crafted networks on image classification, particularly, the best network generated by BlockQNN achieves 2. 35% top-1 error rate on CIFAR-10.

Image Classification Q-Learning

Unleashing the Potential of CNNs for Interpretable Few-Shot Learning

no code implementations ICLR 2018 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs.

Few-Shot Learning

Peephole: Predicting Network Performance Before Training

1 code implementation9 Dec 2017 Boyang Deng, Junjie Yan, Dahua Lin

The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years.

Few-shot Learning by Exploiting Visual Concepts within CNNs

no code implementations22 Nov 2017 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning.

Few-Shot Learning

Hierarchical Deep Recurrent Architecture for Video Understanding

1 code implementation11 Jul 2017 Luming Tang, Boyang Deng, Haiyu Zhao, Shuai Yi

The proposed framework contains hierarchical deep architecture, including the frame-level sequence modeling part and the video-level classification part.

Classification Frame +3

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