309 citations · 498 across the 16 of their papers we have counts for
17 papers
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Shuo Wang, Xiangyu Wang, Quanxin Wang +9
Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlyi…
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Shuo Wang, Bokui Wang, Zhixiang Shen +2
Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental chall…
LumiGAN: Unconditional Generation of Relightable 3D Human Faces
Boyang Deng, Yifan Wang, Gordon Wetzstein
Unsupervised learning of 3D human faces from unstructured 2D image data is an active research area. While recent works have achieved an impressive level of photorealism, they commo…
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Bokui Shen, Xinchen Yan, Charles R. Qi +5
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.…
Adding New Categories in Object Detection Using Few-Shot Copy-Paste
Boyang Deng, Meiyan Lin, Shoulun Long
Developing data-efficient instance detection models that can handle rare object categories remains a key challenge in computer vision. However, existing research often overlooks da…
Revisiting 3D Object Detection From an Egocentric Perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi +3
3D object detection is a key module for safety-critical robotics applications such as autonomous driving. For these applications, we care most about how the detections affect the e…
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng +3
We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknow…
Offboard 3D Object Detection from Point Cloud Sequences
Charles R. Qi, Yin Zhou, Mahyar Najibi +4
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, suc…
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang +3
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…
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng +4
We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise th…
NASA: Neural Articulated Shape Approximation
Boyang Deng, JP Lewis, Timothy Jeruzalski +4
Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approach…
CvxNet: Learnable Convex Decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani +3
Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a…
Cerberus: A Multi-headed Derenderer
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 c…
BlockQNN: Efficient Block-wise Neural Network Architecture Generation
Zhao Zhong, Zichen Yang, Boyang Deng +4
Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and ela…
Peephole: Predicting Network Performance Before Training
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. While rewarding, improving network design has never be…
Few-shot Learning by Exploiting Visual Concepts within CNNs
Boyang Deng, Qing Liu, Siyuan Qiao +1
Convolutional neural networks (CNNs) are one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major…
Hierarchical Deep Recurrent Architecture for Video Understanding
Luming Tang, Boyang Deng, Haiyu Zhao +1
This paper introduces the system we developed for the Youtube-8M Video Understanding Challenge, in which a large-scale benchmark dataset was used for multi-label video classificati…