no code implementations • 6 Dec 2023 • Xujie Zhang, Xiu Li, Michael Kampffmeyer, Xin Dong, Zhenyu Xie, Feida Zhu, Haoye Dong, Xiaodan Liang
Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person.
no code implementations • 26 Sep 2023 • Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
An ideal detection model is expected to achieve all three critical properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities.
1 code implementation • 7 Aug 2023 • Huichao Zhang, Bowen Chen, Hao Yang, Liao Qu, Xu Wang, Li Chen, Chao Long, Feida Zhu, Kang Du, Min Zheng
We present AvatarVerse, a stable pipeline for generating expressive high-quality 3D avatars from nothing but text descriptions and pose guidance.
no code implementations • 29 Jul 2023 • Zuyan Liu, Gaojie Lin, Congyi Wang, Min Zheng, Feida Zhu
Our approach involves a unified and multi-granularity strategy that includes a pseudo keypoint alignment module in the teacher-student framework for learning pose-aware semantic class tokens.
1 code implementation • 26 Jul 2023 • Sen Zhao, Wei Wei, Xian-Ling Mao, Shuai Zhu, Minghui Yang, Zujie Wen, Dangyang Chen, Feida Zhu
Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views.
no code implementations • CVPR 2023 • Congyi Wang, Feida Zhu, Shilei Wen
Existing methods proposed for hand reconstruction tasks usually parameterize a generic 3D hand model or predict hand mesh positions directly.
1 code implementation • CVPR 2023 • Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao, Haoye Dong, Xijin Zhang, Feida Zhu, Xiaodan Liang
Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs. On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem.
no code implementations • 13 Jan 2023 • Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
To detect fraud behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor.
no code implementations • 24 Sep 2022 • Ling Cheng, Feida Zhu, Yong Wang, Huiwen Liu
% With the type-dependent selection strategy and global status vectors, our model can be applied to detect various illicit activities with strong interpretability.
no code implementations • 4 Sep 2022 • Xin Mu, Ming Pang, Feida Zhu
In this paper, we introduce Data Provenance via Differential Auditing (DPDA), a practical framework for auditing data provenance with a different approach based on statistically significant differentials, i. e., after carefully designed transformation, perturbed input data from the target model's training set would result in much more drastic changes in the output than those from the model's non-training set.
1 code implementation • 22 Aug 2022 • Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen
Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR.
no code implementations • 27 Jul 2022 • Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xin Dong, Feida Zhu, Xiaodan Liang
In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing.
1 code implementation • 6 May 2022 • Weiran Pan, Wei Wei, Feida Zhu
Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications.
1 code implementation • 5 May 2022 • Yuhang Liu, Wei Wei, Daowan Peng, Feida Zhu
In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e. g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e. g., VQA) via a brand-new objective function, e. g., answer prediction.
1 code implementation • 19 Apr 2022 • Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao
Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.
no code implementations • CVPR 2022 • Feida Zhu, Junwei Zhu, Wenqing Chu, Xinyi Zhang, Xiaozhong Ji, Chengjie Wang, Ying Tai
Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task.
1 code implementation • 15 Dec 2021 • Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu
IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models.
no code implementations • 29 Sep 2021 • Ling Cheng, Wei Wei, Feida Zhu, Yong liu, Chunyan Miao
However, those fusion-based models, they are still criticized for the lack of geometry information for inter and intra attention refinement.
no code implementations • 31 Aug 2021 • Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph.
no code implementations • 18 Aug 2021 • Zicun Cong, Xuan Luo, Pei Jian, Feida Zhu, Yong Zhang
We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment.
no code implementations • 6 Jun 2021 • Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
no code implementations • 15 Nov 2020 • Wei Wei, Jiayi Liu, Xianling Mao, Guibin Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
no code implementations • 22 Aug 2020 • Feida Zhu, Chaowei Fang, Kai-Kuang Ma
Additionally, the pyramid non-local block can be directly incorporated into convolution neural networks for other image restoration tasks.
no code implementations • 3 Jul 2020 • Shanyan Guan, Ying Tai, Bingbing Ni, Feida Zhu, Feiyue Huang, Xiaokang Yang
The latent code of the recent popular model StyleGAN has learned disentangled representations thanks to the multi-layer style-based generator.
no code implementations • 5 Sep 2019 • Wei Wei, Ling Cheng, Xian-Ling Mao, Guangyou Zhou, Feida Zhu
Recently, automatic image caption generation has been an important focus of the work on multimodal translation task.
1 code implementation • 2 Apr 2019 • Feida Zhu, Zhetong Liang, Xixi Jia, Lei Zhang, Yizhou Yu
This benchmark includes an image dataset with groundtruth image smoothing results as well as baseline algorithms that can generate competitive edge-preserving smoothing results for a wide range of image contents.
1 code implementation • 22 Mar 2019 • Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process.
no code implementations • 27 Nov 2018 • Feida Zhu, Yizhou Yu
Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent.
1 code implementation • 24 Sep 2018 • Sein Minn, Yi Yu, Michel C. Desmarais, Feida Zhu, Jill Jenn Vie
In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions.