Search Results for author: Jinghua Wang

Found 13 papers, 0 papers with code

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

no code implementations27 Oct 2023 Jiesi Hu, Yanwu Yang, Xutao Guo, Jinghua Wang, Ting Ma

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data.

Denoising Image Segmentation +3

CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection

no code implementations CVPR 2023 Yabo Liu, Jinghua Wang, Chao Huang, YaoWei Wang, Yong Xu

To overcome these problems, we propose a cross-modality graph reasoning adaptation (CIGAR) method to take advantage of both visual and linguistic knowledge.

Graph Matching object-detection +1

Few-Shot Classification with Contrastive Learning

no code implementations17 Sep 2022 Zhanyuan Yang, Jinghua Wang, Yingying Zhu

In the meta-training stage, we propose a cross-view episodic training mechanism to perform the nearest centroid classification on two different views of the same episode and adopt a distance-scaled contrastive loss based on them.

Classification Contrastive Learning +2

Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition

no code implementations3 Sep 2022 Tianjiao Li, Lin Geng Foo, Qiuhong Ke, Hossein Rahmani, Anran Wang, Jinghua Wang, Jun Liu

We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.

Fine-grained Action Recognition

Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network

no code implementations12 Feb 2021 Kanya Mo, Shen Zheng, Xiwei Wang, Jinghua Wang, Klaus-Dieter Schewe

The fully connected (FC) layer, one of the most fundamental modules in artificial neural networks (ANN), is often considered difficult and inefficient to train due to issues including the risk of overfitting caused by its large amount of parameters.

Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation

no code implementations ICCV 2019 Jinghua Wang, Jianmin Jiang

To train CoCoGAN in the absence of target-domain data for RT, we propose a new supervisory signal, i. e. the alignment between representations across tasks.

Domain Adaptation

An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling

no code implementations11 Sep 2020 Jinghua Wang, Jianmin Jiang

In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized to achieve the best possible unsupervised learning and knowledge discovery from unlabeled data sets.

Clustering Representation Learning

Adversarial Learning for Zero-shot Domain Adaptation

no code implementations ECCV 2020 Jinghua Wang, Jianmin Jiang

With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain shift from an irrelevant task (IrT) to the task of interest (ToI).

Domain Adaptation

Spectral Analysis Network for Deep Representation Learning and Image Clustering

no code implementations11 Sep 2020 Jinghua Wang, Adrian Hilton, Jianmin Jiang

This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations.

Clustering Image Clustering +1

SA-Net: A deep spectral analysis network for image clustering

no code implementations11 Sep 2020 Jinghua Wang, Jianmin Jiang

In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering.

Clustering Image Clustering +1

Towards Predicting the Likeability of Fashion Images

no code implementations17 Nov 2015 Jinghua Wang, Abrar Abdul Nabi, Gang Wang, Chengde Wan, Tian-Tsong Ng

Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images.

Attribute

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