no code implementations • 20 Feb 2024 • Jianhong Bai, Tianyu He, Yuchi Wang, Junliang Guo, Haoji Hu, Zuozhu Liu, Jiang Bian
Recent advances in text-guided video editing have showcased promising results in appearance editing (e. g., stylization).
no code implementations • 30 Nov 2023 • Lianrui Mu, Jianhong Bai, Xiaoxuan He, Jiangnan Ye, Xiaoyu Liang, Yuchen Yang, Jiedong Zhuang, Haoji Hu
Enhancing the domain generalization performance of Face Anti-Spoofing (FAS) techniques has emerged as a research focus.
no code implementations • 24 Nov 2023 • Xiaoxuan He, Yifan Yang, Xinyang Jiang, Xufang Luo, Haoji Hu, Siyun Zhao, Dongsheng Li, Yuqing Yang, Lili Qiu
To overcome the aforementioned challenges, we propose an Unified Medical Image Pre-training framework, namely UniMedI, which utilizes diagnostic reports as common semantic space to create unified representations for diverse modalities of medical images (especially for 2D and 3D images).
no code implementations • 5 Oct 2023 • Jianhong Bai, Yuchen Yang, Huanpeng Chu, Hualiang Wang, Zuozhu Liu, Ruizhe Chen, Xiaoxuan He, Lianrui Mu, Chengfei Cai, Haoji Hu
Quantization has emerged as a promising direction for model compression.
1 code implementation • NeurIPS 2023 • Jianhong Bai, Zuozhu Liu, Hualiang Wang, Ruizhe Chen, Lianrui Mu, Xiaomeng Li, Joey Tianyi Zhou, Yang Feng, Jian Wu, Haoji Hu
In this paper, we formally define a more realistic task as distribution-agnostic generalized category discovery (DA-GCD): generating fine-grained predictions for both close- and open-set classes in a long-tailed open-world setting.
no code implementations • 28 Jun 2023 • Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, Cyrus Shahabi
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
2 code implementations • 8 Jun 2023 • Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, Haoji Hu
Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect.
no code implementations • 20 Jan 2023 • Haoji Hu, Haowen Lin, Yao-Yi Chiang
Human mobility clustering is an important problem for understanding human mobility behaviors (e. g., work and school commutes).
1 code implementation • 22 Aug 2022 • Hualiang Wang, Siming Fu, Xiaoxuan He, Hangxiang Fang, Zuozhu Liu, Haoji Hu
To our knowledge, this is the first work to measure representation quality of classifiers and features from the perspective of distribution overlap coefficient.
no code implementations • 11 Mar 2022 • Jin Hao, Jiaxiang Liu, Jin Li, Wei Pan, Ruizhe Chen, Huimin Xiong, Kaiwei Sun, Hangzheng Lin, Wanlu Liu, Wanghui Ding, Jianfei Yang, Haoji Hu, Yueling Zhang, Yang Feng, Zeyu Zhao, Huikai Wu, Youyi Zheng, Bing Fang, Zuozhu Liu, Zhihe Zhao
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
no code implementations • 17 Jan 2022 • Chen Lin, Zheyang Li, Bo Peng, Haoji Hu, Wenming Tan, Ye Ren, ShiLiang Pu
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance.
no code implementations • 4 Jun 2021 • Kuan Zhang, Haoji Hu, Kenneth Philbrick, Gian Marco Conte, Joseph D. Sobek, Pouria Rouzrokh, Bradley J. Erickson
There is a growing demand for high-resolution (HR) medical images in both the clinical and research applications.
1 code implementation • 16 Sep 2020 • Bianjiang Yang, Zi Hui, Haoji Hu, Xinyi Hu, Lu Yu
Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture.
2 code implementations • 31 May 2020 • Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
Ranked #1 on Next-basket recommendation on TaFeng
1 code implementation • CVPR 2020 • Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan Yang
In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters.
no code implementations • 31 Jan 2020 • Xinyue Hu, Haoji Hu, Saurabh Verma, Zhi-Li Zhang
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
no code implementations • NIPS Workshop CDNNRIA 2018 • Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss.
no code implementations • NIPS Workshop CDNNRIA 2018 • Yuxin Zhang, Huan Wang, Yang Luo, Lu Yu, Haoji Hu, Hangguan Shan, Tony Q. S. Quek
Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs)are restricted by their massive computation and storage consumption.
1 code implementation • 25 Apr 2018 • Huan Wang, Qiming Zhang, Yuehai Wang, Yu Lu, Haoji Hu
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degrade.
2 code implementations • 20 Sep 2017 • Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu
Unlike existing deterministic pruning approaches, where unimportant weights are permanently eliminated, SPP introduces a pruning probability for each weight, and pruning is guided by sampling from the pruning probabilities.