Search Results for author: Zunlei Feng

Found 35 papers, 19 papers with code

Model Doctor for Diagnosing and Treating Segmentation Error

1 code implementation17 Feb 2023 Zhijie Jia, Lin Chen, Kaiwen Hu, Lechao Cheng, Zunlei Feng, Mingli Song

Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries.

Semantic Segmentation

Team DETR: Guide Queries as a Professional Team in Detection Transformers

1 code implementation14 Feb 2023 Tian Qiu, Linyun Zhou, Wenxiang Xu, Lechao Cheng, Zunlei Feng, Mingli Song

Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance.

Recent advances in artificial intelligence for retrosynthesis

no code implementations14 Jan 2023 Zipeng Zhong, Jie Song, Zunlei Feng, Tiantao Liu, Lingxiang Jia, Shaolun Yao, Tingjun Hou, Mingli Song

Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets.

Multi-step retrosynthesis

Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

1 code implementation30 Nov 2022 Siqi Fan, Fenghua Zhu, Zunlei Feng, Yisheng Lv, Mingli Song, Fei-Yue Wang

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels.

Semi-Supervised Semantic Segmentation

Transferability Estimation Based On Principal Gradient Expectation

no code implementations29 Nov 2022 Huiyan Qi, Lechao Cheng, Jingjing Chen, Yue Yu, Xue Song, Zunlei Feng, Yu-Gang Jiang

Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks.

Transfer Learning

Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

1 code implementation23 Nov 2022 Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng, KaiXuan Chen, Tongtian Zhu, Zunlei Feng, Mingli Song

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.

Contrastive Learning SMAC+

Federated Selective Aggregation for Knowledge Amalgamation

no code implementations27 Jul 2022 Donglin Xie, Ruonan Yu, Gongfan Fang, Jie Song, Zunlei Feng, Xinchao Wang, Li Sun, Mingli Song

The goal of FedSA is to train a student model for a new task with the help of several decentralized teachers, whose pre-training tasks and data are different and agnostic.

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

1 code implementation8 Jul 2022 Shunyu Liu, Jie Song, Yihe Zhou, Na Yu, KaiXuan Chen, Zunlei Feng, Mingli Song

In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.

Multi-agent Reinforcement Learning reinforcement-learning +1

Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework

no code implementations5 Jul 2022 Shunyu Liu, Na Yu, Jie Song, KaiXuan Chen, Zunlei Feng, Mingli Song

Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

1 code implementation12 May 2022 KaiXuan Chen, Shunyu Liu, Na Yu, Rong Yan, Quan Zhang, Jie Song, Zunlei Feng, Mingli Song

As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.

Graph Representation Learning Numerical Integration

Comparison Knowledge Translation for Generalizable Image Classification

1 code implementation7 May 2022 Zunlei Feng, Tian Qiu, Sai Wu, Xiaotuan Jin, Zengliang He, Mingli Song, Huiqiong Wang

In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories.

Classification Image Classification +1

Knowledge Amalgamation for Object Detection with Transformers

no code implementations7 Mar 2022 Haofei Zhang, Feng Mao, Mengqi Xue, Gongfan Fang, Zunlei Feng, Jie Song, Mingli Song

Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.

object-detection Object Detection

Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN

no code implementations16 Dec 2021 Gengshi Han, Shunyu Liu, KaiXuan Chen, Na Yu, Zunlei Feng, Mingli Song

This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.

Learning Dynamic Preference Structure Embedding From Temporal Networks

1 code implementation23 Nov 2021 Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu

Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.

Graph Sampling

Distribution Knowledge Embedding for Graph Pooling

1 code implementation29 Sep 2021 KaiXuan Chen, Jie Song, Shunyu Liu, Na Yu, Zunlei Feng, Gengshi Han, Mingli Song

A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.

Representation Learning

Boundary Knowledge Translation based Reference Semantic Segmentation

no code implementations1 Aug 2021 Lechao Cheng, Zunlei Feng, Xinchao Wang, Ya Jie Liu, Jie Lei, Mingli Song

In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation.

Semantic Segmentation Translation

Visual Boundary Knowledge Translation for Foreground Segmentation

1 code implementation1 Aug 2021 Zunlei Feng, Lechao Cheng, Xinchao Wang, Xiang Wang, Yajie Liu, Xiangtong Du, Mingli Song

To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.

Image Segmentation Semantic Segmentation +1

Edge-competing Pathological Liver Vessel Segmentation with Limited Labels

1 code implementation1 Aug 2021 Zunlei Feng, Zhonghua Wang, Xinchao Wang, Xiuming Zhang, Lechao Cheng, Jie Lei, Yuexuan Wang, Mingli Song

The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.

whole slide images

Shape Controllable Virtual Try-on for Underwear Models

no code implementations28 Jul 2021 Xin Gao, Zhenjiang Liu, Zunlei Feng, Chengji Shen, Kairi Ou, Haihong Tang, Mingli Song

Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.

Graph Attention Virtual Try-on

One-sample Guided Object Representation Disassembling

no code implementations NeurIPS 2020 Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song

In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.

Data Augmentation Image Classification

DEAL: Difficulty-aware Active Learning for Semantic Segmentation

1 code implementation17 Oct 2020 Shuai Xie, Zunlei Feng, Ying Chen, Songtao Sun, Chao Ma, Mingli Song

To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch.

Active Learning Semantic Segmentation

Factorizable Graph Convolutional Networks

1 code implementation NeurIPS 2020 Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang

In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.

Graph Classification Graph Regression +1

Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization

no code implementations13 Apr 2020 Xinhui Song, Tianyang Shi, Tianjia Shao, Yi Yuan, Zunlei Feng, Changjie Fan

The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image.

Disassembling Object Representations without Labels

no code implementations3 Apr 2020 Zunlei Feng, Xinchao Wang, Yongming He, Yike Yuan, Xin Gao, Mingli Song

In this paper, we study a new representation-learning task, which we termed as disassembling object representations.

General Classification Representation Learning +1

Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift

no code implementations18 Dec 2019 Da Chen, Yong-Liang Yang, Zunlei Feng, Xiang Wu, Mingli Song, Wenbin Li, Yuan He, Hui Xue, Feng Mao

This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.

Few-Shot Image Classification General Classification +1

Interpretable Partitioned Embedding for Customized Fashion Outfit Composition

no code implementations13 Jun 2018 Zunlei Feng, Zhenyun Yu, Yezhou Yang, Yongcheng Jing, Junxiao Jiang, Mingli Song

In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes.

Dual Swap Disentangling

1 code implementation NeurIPS 2018 Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, DaCheng Tao, Mingli Song

To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

1 code implementation ECCV 2018 Yongcheng Jing, Yang Liu, Yezhou Yang, Zunlei Feng, Yizhou Yu, DaCheng Tao, Mingli Song

In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.

Style Transfer

Neural Style Transfer: A Review

8 code implementations11 May 2017 Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, Mingli Song

We first propose a taxonomy of current algorithms in the field of NST.

Style Transfer

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