Search Results for author: Yue Fan

Found 14 papers, 5 papers with code

An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning

no code implementations20 Nov 2022 Hao Chen, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Marios Savvides, Bhiksha Raj

While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.

Pseudo Label

JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents

no code implementations28 Aug 2022 Kaizhi Zheng, Kaiwen Zhou, Jing Gu, Yue Fan, Jialu Wang, Zonglin Di, Xuehai He, Xin Eric Wang

Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc.

Action Generation Common Sense Reasoning +1

Aerial Vision-and-Dialog Navigation

no code implementations24 May 2022 Yue Fan, Winson Chen, Tongzhou Jiang, Chun Zhou, Yi Zhang, Xin Eric Wang

To this end, we introduce Aerial Vision-and-Dialog Navigation (AVDN), to navigate a drone via natural language conversation.

Navigate

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

1 code implementation15 May 2022 Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie

Based on the analysis, we hence propose FreeMatch to define and adjust the confidence threshold in a self-adaptive manner according to the model's learning status.

Fairness

Revisiting Consistency Regularization for Semi-Supervised Learning

no code implementations10 Dec 2021 Yue Fan, Anna Kukleva, Bernt Schiele

Generally, the aim is to train a model that is invariant to various data augmentations.

CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning

1 code implementation CVPR 2022 Yue Fan, Dengxin Dai, Anna Kukleva, Bernt Schiele

In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL.

Representation Learning

Multi-Vector Embedding on Networks with Taxonomies

no code implementations29 Sep 2021 Yue Fan, Xiuli Ma

Networks serve as efficient tools to describe close relationships among nodes.

Network Embedding

Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator

no code implementations30 Aug 2020 Yue Fan, Shilei Chu, Wei zhang, Ran Song, Yibin Li

Extensive experiments are conducted to demonstrate the accuracy of the proposed imitating learning process as well as the reliability of the holistic system for autonomous drone navigation.

Drone navigation Imitation Learning

Analyzing the Dependency of ConvNets on Spatial Information

no code implementations5 Feb 2020 Yue Fan, Yongqin Xian, Max Maria Losch, Bernt Schiele

In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information.

Image Classification Object Recognition

CN-CELEB: a challenging Chinese speaker recognition dataset

1 code implementation31 Oct 2019 Yue Fan, Jiawen Kang, Lantian Li, Kaicheng Li, Haolin Chen, Sitong Cheng, Pengyuan Zhang, Ziya Zhou, Yunqi Cai, Dong Wang

These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.

Speaker Recognition

Tag2Vec: Learning Tag Representations in Tag Networks

no code implementations19 Apr 2019 Junshan Wang, Zhicong Lu, Guojie Song, Yue Fan, Lun Du, Wei. Lin

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.

Network Embedding TAG

Parameter-Free Spatial Attention Network for Person Re-Identification

3 code implementations29 Nov 2018 Haoran Wang, Yue Fan, Zexin Wang, Licheng Jiao, Bernt Schiele

We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.

Person Re-Identification

Feature vector regularization in machine learning

no code implementations19 Dec 2012 Yue Fan, Louise Raphael, Mark Kon

Such feature vector regularization inherits a property from function denoising on ${\bf R}^n$, in that accuracy is non-monotonic in the denoising (regularization) parameter $\alpha$.

BIG-bench Machine Learning Denoising +2

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