Search Results for author: Tangjie Lv

Found 25 papers, 7 papers with code

A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment

1 code implementation10 Mar 2024 Fei Wang, Haoyu Liu, Haoyang Bi, Xiangzhuang Shen, Renyu Zhu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Qi Liu, Zhenya Huang, Enhong Chen

In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.

Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation

no code implementations15 Feb 2024 Jiashu Pu, Yajing Wan, Yuru Zhang, Jing Chen, Ling Cheng, Qian Shao, Yongzhu Chang, Tangjie Lv, Rongsheng Zhang

Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce.

Dialogue Generation In-Context Learning +1

Towards Long-term Annotators: A Supervised Label Aggregation Baseline

no code implementations15 Nov 2023 Haoyu Liu, Fei Wang, Minmin Lin, Runze Wu, Renyu Zhu, Shiwei Zhao, Kai Wang, Tangjie Lv, Changjie Fan

These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works.

AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model

no code implementations3 Oct 2023 Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Tangjie Lv, Changjie Fan, Zhipeng Hu

Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.

Attribute Reinforcement Learning (RL)

Examining the Effect of Pre-training on Time Series Classification

no code implementations11 Sep 2023 Jiashu Pu, Shiwei Zhao, Ling Cheng, Yongzhu Chang, Runze Wu, Tangjie Lv, Rongsheng Zhang

(iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence.

Time Series Time Series Classification +1

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

1 code implementation28 Jul 2023 Renyu Zhu, Haoyu Liu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Haobo Wang

In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.

Learning with noisy labels

Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning

no code implementations27 Jun 2023 Jinyi Liu, Yi Ma, Jianye Hao, Yujing Hu, Yan Zheng, Tangjie Lv, Changjie Fan

In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL algorithms.

D4RL Offline RL +2

FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping

no code implementations22 Jun 2023 Yu Zhang, Hao Zeng, Bowen Ma, Wei zhang, Zhimeng Zhang, Yu Ding, Tangjie Lv, Changjie Fan

The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results.

Decoder Face Swapping

Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations

2 code implementations6 May 2023 Yufeng Huang, Jiji Tang, Zhuo Chen, Rongsheng Zhang, Xinfeng Zhang, WeiJie Chen, Zeng Zhao, Zhou Zhao, Tangjie Lv, Zhipeng Hu, Wen Zhang

In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.

Image-text matching Text Matching

TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles

no code implementations1 Apr 2023 Yifeng Ma, Suzhen Wang, Yu Ding, Bowen Ma, Tangjie Lv, Changjie Fan, Zhipeng Hu, Zhidong Deng, Xin Yu

In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language.

2D Semantic Segmentation task 3 (25 classes) Talking Head Generation

DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video

1 code implementation7 Mar 2023 Zhimeng Zhang, Zhipeng Hu, Wenjin Deng, Changjie Fan, Tangjie Lv, Yu Ding

Different from previous works relying on multiple up-sample layers to directly generate pixels from latent embeddings, DINet performs spatial deformation on feature maps of reference images to better preserve high-frequency textural details.

Decoder Face Dubbing

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

no code implementations7 Feb 2023 Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan

Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.

Multi-agent Reinforcement Learning reinforcement-learning +1

StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles

1 code implementation3 Jan 2023 Yifeng Ma, Suzhen Wang, Zhipeng Hu, Changjie Fan, Tangjie Lv, Yu Ding, Zhidong Deng, Xin Yu

In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio.

Decoder Talking Face Generation +1

TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective

no code implementations17 Dec 2022 Pengfei Xi, Guifeng Wang, Zhipeng Hu, Yu Xiong, Mingming Gong, Wei Huang, Runze Wu, Yu Ding, Tangjie Lv, Changjie Fan, Xiangnan Feng

TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.

Contrastive Learning counterfactual +3

FlowFace: Semantic Flow-guided Shape-aware Face Swapping

no code implementations6 Dec 2022 Hao Zeng, Wei zhang, Changjie Fan, Tangjie Lv, Suzhen Wang, Zhimeng Zhang, Bowen Ma, Lincheng Li, Yu Ding, Xin Yu

Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.

Face Swapping

Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation

no code implementations28 Oct 2022 Bowen Ma, Rudong An, Wei zhang, Yu Ding, Zeng Zhao, Rongsheng Zhang, Tangjie Lv, Changjie Fan, Zhipeng Hu

As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.

Action Unit Detection Facial Action Unit Detection

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

1 code implementation26 Apr 2022 Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu

While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"

Collaborative Filtering Recommendation Systems

Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction

no code implementations25 Sep 2018 Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Zhaopeng Meng, Changjie Fan, Li Wang

Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.

reinforcement-learning Reinforcement Learning (RL)

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