Search Results for author: Feida Zhu

Found 30 papers, 11 papers with code

From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency

no code implementations26 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.

feature selection

AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose

1 code implementation7 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.

Text-to-3D-Human Generation

HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation

no code implementations29 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.

Pose Estimation regression +2

Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation

1 code implementation26 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.

Recommendation Systems

MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction

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.

Vocal Bursts Valence Prediction

GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

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.

Virtual Try-on

Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency

no code implementations13 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.

Toward Intention Discovery for Early Malice Detection in Bitcoin

no code implementations24 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.

feature selection

Data Provenance via Differential Auditing

no code implementations4 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.

PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

no code implementations27 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.

Disentanglement Image Generation +1

Automatic Noisy Label Correction for Fine-Grained Entity Typing

1 code implementation6 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.

Entity Typing

Declaration-based Prompt Tuning for Visual Question Answering

1 code implementation5 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.

Image-text matching Language Modelling +5

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

1 code implementation19 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.

Contrastive Learning Data Augmentation +2

Blind Face Restoration via Integrating Face Shape and Generative Priors

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.

3D Reconstruction Blind Face Restoration

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

1 code implementation15 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.

Knowledge Tracing Skill Mastery

Geometry-Entangled Visual Semantic Transformer for Image Captioning

no code implementations29 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.

Image Captioning

Heterogeneous Graph Neural Network with Multi-view Representation Learning

no code implementations31 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.

Graph Embedding Link Prediction +3

Data Pricing in Machine Learning Pipelines

no code implementations18 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.

BIG-bench Machine Learning

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

no code implementations6 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.

Response Generation

Target Guided Emotion Aware Chat Machine

no code implementations15 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.

PNEN: Pyramid Non-Local Enhanced Networks

no code implementations22 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.

Image Denoising Image Restoration +2

Collaborative Learning for Faster StyleGAN Embedding

no code implementations3 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.

Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

no code implementations5 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.


A Benchmark for Edge-Preserving Image Smoothing

1 code implementation2 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.

image smoothing

Dynamic Student Classiffication on Memory Networks for Knowledge Tracing

1 code implementation22 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.

Knowledge Tracing

Automatic Image Stylization Using Deep Fully Convolutional Networks

no code implementations27 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.

Image Stylization

Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing

1 code implementation24 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.

Classification General Classification +1

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