2 code implementations • 15 Jan 2024 • Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, Anthony Chen, Huaxia Li, Xu Tang, Yao Hu
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA.
Ranked #2 on Diffusion Personalization Tuning Free on AgeDB
9 code implementations • 3 Oct 2019 • Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
2 code implementations • 25 Jun 2020 • Haofan Wang, Rakshit Naidu, Joy Michael, Soumya Snigdha Kundu
Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments.
1 code implementation • 3 Apr 2024 • Haofan Wang, Matteo Spinelli, Qixun Wang, Xu Bai, Zekui Qin, Anthony Chen
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization.
2 code implementations • 6 Feb 2023 • Qixun Wang, Xiaofeng Guo, Haofan Wang
Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes.
1 code implementation • 21 Jan 2019 • Haofan Wang, Zhenghua Chen, Yi Zhou
In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network.
Ranked #3 on Head Pose Estimation on AFLW
1 code implementation • 17 Aug 2023 • Liang Pan, Jingbo Wang, Buzhen Huang, Junyu Zhang, Haofan Wang, Xu Tang, Yangang Wang
Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes.
1 code implementation • 4 Nov 2019 • Fan Yang, Zijian Zhang, Haofan Wang, Yuening Li, Xia Hu
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
1 code implementation • 15 Dec 2020 • Shentong Mo, Haofan Wang, Pinxu Ren, Ta-Chung Chi
Automatic speech verification (ASV) is the technology to determine the identity of a person based on their voice.
1 code implementation • 19 Dec 2023 • Pengxiang Ding, Qiongjie Cui, Min Zhang, Mengyuan Liu, Haofan Wang, Donglin Wang
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
1 code implementation • NeurIPS 2020 • Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, Anupam Datta
Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent explanations for nearby inputs.
no code implementations • 2 Oct 2019 • Zijian Zhang, Fan Yang, Haofan Wang, Xia Hu
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.
no code implementations • 24 Jun 2021 • Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, FatemehSadat Mireshghallah
Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off.
no code implementations • 10 Sep 2021 • Jue Wang, Haofan Wang, Jincan Deng, Weijia Wu, Debing Zhang
Extra rich non-paired single-modal text data is used for boosting the generalization of text branch.
no code implementations • 30 Oct 2021 • Jue Wang, Haofan Wang, Xing Wu, Chaochen Gao, Debing Zhang
In this paper, we present TransAug (Translate as Augmentation), which provide the first exploration of utilizing translated sentence pairs as data augmentation for text, and introduce a two-stage paradigm to advances the state-of-the-art sentence embeddings.
no code implementations • 11 Jul 2022 • Jinbin Bai, Chunhui Liu, Feiyue Ni, Haofan Wang, Mengying Hu, Xiaofeng Guo, Lele Cheng
To overcome the above issue, we present a novel mechanism for learning the translation relationship from a source modality space $\mathcal{S}$ to a target modality space $\mathcal{T}$ without the need for a joint latent space, which bridges the gap between visual and textual domains.
Ranked #11 on Zero-Shot Video Retrieval on MSVD
no code implementations • ICCV 2023 • Yangyi Huang, Hongwei Yi, Weiyang Liu, Haofan Wang, Boxi Wu, Wenxiao Wang, Binbin Lin, Debing Zhang, Deng Cai
Most of these methods fail to achieve realistic reconstruction when only a single image is available.
no code implementations • ICCV 2023 • Qiongjie Cui, Huaijiang Sun, Jianfeng Lu, Weiqing Li, Bin Li, Hongwei Yi, Haofan Wang
Current motion forecasting approaches typically train a deep end-to-end model from the source domain data, and then apply it directly to target subjects.
no code implementations • 14 Dec 2023 • Anthony Chen, Huanrui Yang, Yulu Gan, Denis A Gudovskiy, Zhen Dong, Haofan Wang, Tomoyuki Okuno, Yohei Nakata, Shanghang Zhang, Kurt Keutzer
In particular, we build a tree-like Split-Ensemble architecture by performing iterative splitting and pruning from a shared backbone model, where each branch serves as a submodel corresponding to a subtask.