no code implementations • CSRR (ACL) 2022 • Yue Wan, Yueen Ma, Haoxuan You, Zhecan Wang, Shih-Fu Chang
Large-scale visual-linguistic pre-training aims to capture the generic representations from multimodal features, which are essential for downstream vision-language tasks.
no code implementations • 22 Apr 2022 • Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Xiyang Dai, Bin Xiao, Jianwei Yang, Haoxuan You, Kai-Wei Chang, Shih-Fu Chang, Lu Yuan
Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data.
1 code implementation • ICLR 2022 • Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, Yun Fu
We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40
no code implementations • 15 Jan 2022 • Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Jianwei Yang, Xiyang Dai, Bin Xiao, Haoxuan You, Shih-Fu Chang, Lu Yuan
Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51. 9%) and domain-shifted (up to 71. 3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only.
no code implementations • 16 Dec 2021 • Zhecan Wang, Haoxuan You, Liunian Harold Li, Alireza Zareian, Suji Park, Yiqing Liang, Kai-Wei Chang, Shih-Fu Chang
As for pre-training, a scene-graph-aware pre-training method is proposed to leverage structure knowledge extracted in the visual scene graph.
no code implementations • 29 Sep 2021 • Haoxuan You, Luowei Zhou, Bin Xiao, Noel C Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan
Large-scale multimodal contrastive pretraining has demonstrated great utility to support high performance in a range of downstream tasks by mapping multiple modalities into a shared embedding space.
1 code implementation • 8 Jun 2021 • Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, Yue Gao
Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data.
1 code implementation • NAACL 2021 • Liunian Harold Li, Haoxuan You, Zhecan Wang, Alireza Zareian, Shih-Fu Chang, Kai-Wei Chang
Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks.
2 code implementations • ECCV 2020 • Alireza Zareian, Zhecan Wang, Haoxuan You, Shih-Fu Chang
Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild.
1 code implementation • NeurIPS 2019 • Can Qin, Haoxuan You, Lichen Wang, C. -C. Jay Kuo, Yun Fu
Specifically, most general-purpose DA methods that struggle for global feature alignment and ignore local geometric information are not suitable for 3D domain alignment.
Ranked #1 on
Unsupervised Domain Adaptation
on PreSIL to KITTI
no code implementations • ICCV 2019 • Peng Gao, Haoxuan You, Zhanpeng Zhang, Xiaogang Wang, Hongsheng Li
The proposed module learns the cross-modality relationships between latent visual and language summarizations, which summarize visual regions and question into a small number of latent representations to avoid modeling uninformative individual region-word relations.
3 code implementations • 30 Jul 2019 • Min Zhang, Haoxuan You, Pranav Kadam, Shan Liu, C. -C. Jay Kuo
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
no code implementations • 13 Dec 2018 • Gao Peng, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven Hoi, Xiaogang Wang, Hongsheng Li
It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering.
no code implementations • 2 Dec 2018 • Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao
More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the point-multi-view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation.
2 code implementations • 28 Nov 2018 • Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao
However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
1 code implementation • 25 Sep 2018 • Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
no code implementations • 23 Aug 2018 • Haoxuan You, Yifan Feng, Rongrong Ji, Yue Gao
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
no code implementations • 28 Nov 2017 • Haoxuan You, Zhicheng Jiao, Haojun Xu, Jie Li, Ying Wang, Xinbo Gao
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning.