Search Results for author: Jianping Fan

Found 22 papers, 7 papers with code

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

no code implementations8 Apr 2022 Jin Yuan, Feng Hou, Yangzhou Du, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.

Domain Adaptation Self-Supervised Learning

Graph Attention Transformer Network for Multi-Label Image Classification

no code implementations8 Mar 2022 Jin Yuan, Shikai Chen, Yao Zhang, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.

Classification Graph Attention +2

A Survey of Visual Transformers

1 code implementation11 Nov 2021 Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

1 code implementation28 Jun 2021 Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao shi, Jianping Fan, Zhiqiang He

Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.

Brain Tumor Segmentation Transfer Learning +1

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

no code implementations21 Jun 2021 Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao shi, Yang Zhang, Jianping Fan, Zhiqiang He

Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.

Decision Making Image Captioning +1

Cluster-level Feature Alignment for Person Re-identification

1 code implementation15 Aug 2020 Qiuyu Chen, Wei zhang, Jianping Fan

Instance-level alignment is widely exploited for person re-identification, e. g. spatial alignment, latent semantic alignment and triplet alignment.

Person Re-Identification

Automatic Image Labelling at Pixel Level

no code implementations15 Jul 2020 Xiang Zhang, Wei zhang, Jinye Peng, Jianping Fan

A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain.

Image Segmentation Semantic Segmentation

Boundary-aware Context Neural Network for Medical Image Segmentation

no code implementations3 May 2020 Ruxin Wang, Shuyuan Chen, Chaojie Ji, Jianping Fan, Ye Li

In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.

Image Segmentation Medical Image Segmentation +2

Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment

no code implementations CVPR 2020 Qiuyu Chen, Wei zhang, Ning Zhou, Peng Lei, Yi Xu, Yu Zheng, Jianping Fan

Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling.

MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization

1 code implementation27 Oct 2019 Rongcheng Lin, Jing Xiao, Jianping Fan

In this paper, we present and discuss a deep mixture model with online knowledge distillation (MOD) for large-scale video temporal concept localization, which is ranked 3rd in the 3rd YouTube-8M Video Understanding Challenge.

Knowledge Distillation Video Understanding

Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification

no code implementations10 Apr 2019 Jiajie Tian, Zhu Teng, Rui Li, Yan Li, Baopeng Zhang, Jianping Fan

Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e. g. completely different identities and backgrounds) and the intra-dataset difference (e. g. camera invariance).

Person Re-Identification Transfer Learning

Learning Competitive and Discriminative Reconstructions for Anomaly Detection

no code implementations17 Mar 2019 Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.

Anomaly Detection

NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification

1 code implementation12 Nov 2018 Rongcheng Lin, Jing Xiao, Jianping Fan

This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification.

General Classification Video Classification +1

Deep Boosting of Diverse Experts

no code implementations ICLR 2018 Wei Zhang, Qiuyu Chen, Jun Yu, Jianping Fan

In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e. g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.

Object Recognition

Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering

2 code implementations10 Aug 2017 Zhou Yu, Jun Yu, Chenchao Xiang, Jianping Fan, DaCheng Tao

For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.

Question Answering Visual Question Answering +1

Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering

6 code implementations ICCV 2017 Zhou Yu, Jun Yu, Jianping Fan, DaCheng Tao

For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.

Question Answering Visual Question Answering +1

Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition

no code implementations8 Jul 2017 Tianyi Zhao, Baopeng Zhang, Wei zhang, Ning Zhou, Jun Yu, Jianping Fan

Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.

Object Recognition

Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

no code implementations24 Jun 2017 Tianyi Zhao, Jun Yu, Zhenzhong Kuang, Wei zhang, Jianping Fan

In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.

Multi-Task Learning Object Recognition

Re-ranking Object Proposals for Object Detection in Automatic Driving

no code implementations19 May 2016 Zhun Zhong, Mingyi Lei, Shaozi Li, Jianping Fan

In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals.

object-detection Object Detection +2

Efficiently Detecting Overlapping Communities through Seeding and Semi-Supervised Learning

no code implementations23 Jan 2014 Changxing Shang, Shengzhong Feng, Zhongying Zhao, Jianping Fan

This paper proposes a new method that transforms a network into a corpus where each edge is treated as a document, and all nodes of the network are treated as terms of the corpus.

Community Detection

Correlative Multi-Label Multi-Instance Image Annotation

no code implementations IEEE International Conference on Computer Vision 2011 Xiangyang Xue, Wei zhang, Jie Zhang, Bin Wu, Jianping Fan, Yao Lu

The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.

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