Search Results for author: Yiming Gao

Found 13 papers, 3 papers with code

Advances in 3D Generation: A Survey

no code implementations31 Jan 2024 Xiaoyu Li, Qi Zhang, Di Kang, Weihao Cheng, Yiming Gao, Jingbo Zhang, Zhihao Liang, Jing Liao, Yan-Pei Cao, Ying Shan

In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.

Novel View Synthesis

Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain

no code implementations28 Jan 2024 Yiming Gao, Feiyu Liu, Liang Wang, Zhenjie Lian, Dehua Zheng, Weixuan Wang, Wenjin Yang, Siqin Li, Xianliang Wang, Wenhui Chen, Jing Dai, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e. g., winning games).

Prompt Guided Copy Mechanism for Conversational Question Answering

no code implementations7 Aug 2023 Yong Zhang, Zhitao Li, Jianzong Wang, Yiming Gao, Ning Cheng, Fengying Yu, Jing Xiao

Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions.

Conversational Question Answering

Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective

no code implementations23 Apr 2023 Yiming Gao, Feiyu Liu, Liang Wang, Zhenjie Lian, Weixuan Wang, Siqin Li, Xianliang Wang, Xianhan Zeng, Rundong Wang, Jiawei Wang, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

MOBA games, e. g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far.

IDmUNet: A new image decomposition induced network for sparse feature segmentation

no code implementations5 Mar 2022 Yumeng Ren, Yiming Gao, Chunlin Wu, Xue-Cheng Tai

Because of the sparsity prior and deep unfolding method in the structure design, this IDmUNet combines the advantages of mathematical modeling and data-driven approaches.

Image Segmentation Retinal Vessel Segmentation +2

Learning Diverse Policies in MOBA Games via Macro-Goals

no code implementations NeurIPS 2021 Yiming Gao, Bei Shi, Xueying Du, Liang Wang, Guangwei Chen, Zhenjie Lian, Fuhao Qiu, Guoan Han, Weixuan Wang, Deheng Ye, Qiang Fu, Wei Yang, Lanxiao Huang

Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings.

Dota 2

Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer

2 code implementations26 Jan 2021 Liang Lin, Yiming Gao, Ke Gong, Meng Wang, Xiaodan Liang

Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e. g., sharing discrepant label granularity) without extensive re-training.

Graph Representation Learning Human Parsing +2

Bidirectional Graph Reasoning Network for Panoptic Segmentation

no code implementations CVPR 2020 Yangxin Wu, Gengwei Zhang, Yiming Gao, Xiajun Deng, Ke Gong, Xiaodan Liang, Liang Lin

We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.

Instance Segmentation Panoptic Segmentation +1

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

no code implementations ICCV 2019 Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang

To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales.

Image Retrieval Retrieval

Graphonomy: Universal Human Parsing via Graph Transfer Learning

1 code implementation CVPR 2019 Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin

By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity.

Human Parsing Transfer Learning

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