no code implementations • 27 Mar 2024 • Mukund Varma T, Peihao Wang, Zhiwen Fan, Zhangyang Wang, Hao Su, Ravi Ramamoorthi
In recent years, there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation, style transfer or scene editing, enabled by large-scale 2D image datasets.
no code implementations • 26 Mar 2024 • Jinze Zhao, Peihao Wang, Zhangyang Wang
Specifically, we investigate the impact of the number of data samples, the total number of experts, the sparsity in expert selection, the complexity of the routing mechanism, and the complexity of individual experts.
no code implementations • 31 Dec 2023 • Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
no code implementations • 31 Dec 2023 • Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
1 code implementation • 3 Dec 2023 • Junjie Yang, Jinze Zhao, Peihao Wang, Zhangyang Wang, Yingbin Liang
However, vanilla ControlNet generally requires extensive training of around 5000 steps to achieve a desirable control for a single task.
no code implementations • 5 Oct 2023 • Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang, Hanwen Jiang, Dejia Xu, Zehao Zhu, Dilin Wang, Zhangyang Wang
To address this challenge, we introduce PF-GRT, a new Pose-Free framework for Generalizable Rendering Transformer, eliminating the need for pre-computed camera poses and instead leveraging feature-matching learned directly from data.
1 code implementation • ICCV 2023 • Wenyan Cong, Hanxue Liang, Peihao Wang, Zhiwen Fan, Tianlong Chen, Mukund Varma, Yi Wang, Zhangyang Wang
Cross-scene generalizable NeRF models, which can directly synthesize novel views of unseen scenes, have become a new spotlight of the NeRF field.
no code implementations • 8 Jul 2023 • Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang, Pan Li
To investigate the minimal value of $L$ that achieves sufficient expressive power, we present two set-element embedding layers: (a) linear + power activation (LP) and (b) linear + exponential activations (LE).
1 code implementation • 13 Jun 2023 • Panwang Pan, Zhiwen Fan, Brandon Y. Feng, Peihao Wang, Chenxin Li, Zhangyang Wang
The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality.
1 code implementation • 25 May 2023 • Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang, Dejia Xu, Hanwen Jiang, Zhangyang Wang
To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE).
1 code implementation • NeurIPS 2023 • Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang Wang, Weizhu Chen, Mingyuan Zhou
Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, $e. g.$, as few as 5, 000 images to train from scratch.
no code implementations • 2 Mar 2023 • Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogerio Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic basis.
1 code implementation • ICCV 2023 • Yan Han, Peihao Wang, Souvik Kundu, Ying Ding, Zhangyang Wang
In this paper, we enhance ViG by transcending conventional "pairwise" linkages and harnessing the power of the hypergraph to encapsulate image information.
no code implementations • CVPR 2023 • Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, Zhangyang Wang
In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360deg views that corresponds well with the given reference image.
1 code implementation • 29 Nov 2022 • Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, Zhangyang Wang
In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360{\deg} views that correspond well with the given reference image.
no code implementations • 17 Oct 2022 • Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, Zhangyang Wang
We answer this question by proposing an implicit neural signal processing network, dubbed INSP-Net, via differential operators on INR.
1 code implementation • 14 Oct 2022 • Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau, Ying Ding, Zhangyang Wang
In this paper, firstly, we provide a new perspective of gradient flow to understand the substandard performance of deep GCNs and hypothesize that by facilitating healthy gradient flow, we can significantly improve their trainability, as well as achieve state-of-the-art (SOTA) level performance from vanilla-GCNs.
2 code implementations • 14 Oct 2022 • Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Ranked #2 on Node Property Prediction on ogbn-products
1 code implementation • 19 Sep 2022 • Zhiwen Fan, Peihao Wang, Yifan Jiang, Xinyu Gong, Dejia Xu, Zhangyang Wang
Our framework, called NeRF with Self-supervised Object Segmentation NeRF-SOS, couples object segmentation and neural radiance field to segment objects in any view within a scene.
1 code implementation • 27 Jul 2022 • Mukund Varma T, Peihao Wang, Xuxi Chen, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang
While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages.
Ranked #1 on Generalizable Novel View Synthesis on LLFF
1 code implementation • 14 Jul 2022 • Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li
Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations.
1 code implementation • 8 Jul 2022 • Peihao Wang, Zhiwen Fan, Tianlong Chen, Zhangyang Wang
In this paper, we present a generic INR framework that achieves both data and training efficiency by learning a Neural Implicit Dictionary (NID) from a data collection and representing INR as a functional combination of basis sampled from the dictionary.
1 code implementation • CVPR 2022 • Tianlong Chen, Peihao Wang, Zhiwen Fan, Zhangyang Wang
Inspired by that, we propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training.
1 code implementation • 5 Apr 2022 • Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, Zhangyang Wang
Representing visual signals by implicit representation (e. g., a coordinate based deep network) has prevailed among many vision tasks.
1 code implementation • 2 Apr 2022 • Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang
Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications.
1 code implementation • 9 Mar 2022 • Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang
The first technique, termed AttnScale, decomposes a self-attention block into low-pass and high-pass components, then rescales and combines these two filters to produce an all-pass self-attention matrix.
1 code implementation • CVPR 2022 • Zhiwen Fan, Tianlong Chen, Peihao Wang, Zhangyang Wang
CADTransformer tokenizes directly from the set of graphical primitives in CAD drawings, and correspondingly optimizes line-grained semantic and instance symbol spotting altogether by a pair of prediction heads.
1 code implementation • NeurIPS 2021 • Wenqing Zheng, Qiangqiang Guo, Hao Yang, Peihao Wang, Zhangyang Wang
This paper presents the Delayed Propagation Transformer (DePT), a new transformer-based model that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world.
1 code implementation • 14 Oct 2021 • Peihao Wang, Yuehao Wang, Hua Lin, Jianbo Shi
Graph Convolutional Networks (GCN) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity.
no code implementations • ICLR 2022 • Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang
The first technique, termed AttnScale, decomposes a self-attention block into low-pass and high-pass components, then rescales and combines these two filters to produce an all-pass self-attention matrix.
1 code implementation • 24 Aug 2021 • Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.