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- Yizhi LiuSuggested from co-authorship
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Works242 from public data
- Automated pulmonary nodule detection in CT images using deep convolutional neural networks459
A novel automated pulmonary nodule detection framework with 2D convolutional neural network (CNN) is proposed to assist the CT reading process and it is illustrated that the proposed method can obviously achieve accurate pulmonary nodules detection.
- Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition450
This paper argues that the limited capacity of language models comes from: 1) implicitly language modeling; 2) unidirectional feature representation; 3) language model with noise input, and proposes an autonomous, bidirectional and iterative ABINet for scene text recognition.
- Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection355
A novel single-center loss (SCL) is designed that only compresses intra-class variations of natural faces while boosting inter-class differences in the embedding space and can learn more discriminative features with less optimization difficulty.
- Graph Structured Network for Image-Text Matching311
This paper presents a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence of structured phrase correspondence, and shows that GSMN outperforms state-of-the-art methods on benchmarks.
- ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection257
The ContourNet is proposed, which effectively handles the problems taking a further step toward accurate arbitrary-shaped text detection and suppresses false positives by only outputting predictions with high response value in both orthogonal directions.
- Curriculum Learning for Natural Language Understanding257
By reviewing the trainset in a crossed way, this work is able to distinguish easy examples from difficult ones, and arrange a curriculum for language models, and obtains significant and universal performance improvements on a wide range of NLU tasks.
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- From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network196
A Visual Language Modeling Network (VisionLAN), which views the visual and linguistic information as a union by directly enduing the vision model with language capability, which significantly improves the speed by 39% and adaptively considers the linguistic information to enhance the visual features for accurate recognition.
- Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization196
A novel “Filtration and Distillation Learning” (FDL) model is proposed to enhance the region attention of discriminate parts for FGVC that utilizes the proposing-predicting matchability as the performance metric of Region Proposal Network (RPN), thus enable a direct optimization of RPN to filtrate most discriminative regions.
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- Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning181
A novel Domain-aware Visual Bias Eliminating (DVBE) network is proposed that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately and outperforms existing methods by averaged 5.7% improvement.
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- DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations129
This paper introduces DEADiff, a mecha-nism to decouple the style and semantics of reference images and attains the best visual stylization results and optimal balance between the text controllability inherent in the text-to-image model and style similarity to the reference image, as demonstrated both quantitatively and qualitatively.
- Semi-supervised User Profiling with Heterogeneous Graph Attention Networks110
The authors' heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity.
- PRRNet: Pixel-Region relation network for face forgery detection106
A novel network, called Pixel-Region Relation Network (PRRNet), to capture pixel-wise and region-wise relations respectively for face forgery detection, which achieves the state-of-the-art detection performance on three recent proposed face forgeries detection datasets.
- Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions105
This study proposes an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction, providing a promising solution for EEG-based seizure prediction.
- Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition96
Experiments demonstrate that the proposed Multi-Objective Matrix Normalization (MOMN) method is superior to existing normalization-based methods in terms of both accuracy and efficiency.
- ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting88
This paper argues that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input, and proposes an autonomous, bidirectional and iterative ABINet++ for scene text spotting.
- Convolutional Attention Networks for Scene Text Recognition81
The experimental results validate the effectiveness of different components and show that the convolutional-based method achieves state-of-the-art or competitive performance over prior works, even without the use of RNN.
- Robust Deep Co-Saliency Detection with Group Semantic79
A novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning highlevel group-wise semantic representation as well as deep visual features of a given image group by exploiting the inter-image interaction at semantic-level and the complementarity between group semantics and visual features.
- DiffAM: Diffusion-Based Adversarial Makeup Transfer for Facial Privacy Protection69
A novel face protection approach, dubbed DiffAM, is proposed, which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images.
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- Attention and Language Ensemble for Scene Text Recognition with Convolutional Sequence Modeling63
An attention-based architecture which is completely based on CNNs, and instead of using a single loss from language aspect, multiple losses from attention and language are accumulated for training the networks in an end-to-end way.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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