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Works83 from public data
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- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection284
This work introduces RealNet, a feature reconstruction network with realistic synthetic anomaly and adaptive feature selection, and develops Anomaly-aware Features Selection (A FS), a method for selecting repre-sentative and discriminative pre-trained feature subsets to improve anomaly detection performance while controlling computational costs.
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- Kinship verification from facial images under uncontrolled conditions153
This paper collects a kinship face database by searching 400+ pairs of public figures and celebrities from the internet, and automatically detect them with the Viola-Jones face detector and proposes a new spatial pyramid learning-based (SPLE) feature descriptor for face representation and applies support vector machine (SVM) for kinship verification.
- Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments123
When combined with the discriminative support vector machine (SVM) classifier, GGOP demonstrates the best performance in the authors' experiments, in comparison with several state-of-the-art face feature descriptors.
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- Ensemble similarity learning for kinship verification from facial images in the wild96
Experiments results demonstrate that the proposed Ensemble similarity learning (ESL) method is superior to some state-of-the-art methods in terms of both verification rate and computational efficiency.
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- Learning deep compact similarity metric for kinship verification from face images74
This work proposes a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model, and introduces the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data.
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- Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning64
Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and FERET databases.
- Multi-feature multi-manifold learning for single-sample face recognition54
Experimental results on the widely used AR, FERET and LFW datasets demonstrate the efficacy of the proposed M^3L method, and a multi-feature manifold-manifold distance measure is presented to recognize the probe subjects.
- Kinship verification from facial images by scalable similarity fusion53
This paper proposes a scalable similarity learning (SSL) method for KVFI, which aims to learn a diagonal bilinear similarity model by online truncated gradient learning, which enjoys superiority in scalability and computational efficiency for practical KV FI with high-dimensional data.
- NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results52
The first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4, develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development.
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- Fast single image dehazing based on a regression model42
Experimental results demonstrate that the proposed approach has a lower computational complexity, and the dehazing results are visually appealing even on extremely challenging photos, such as street views, thick fog, and sky regions.
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- Kinship verification in the wild: The first kinship verification competition40
The key goal of this competition is to compare the performance of different methods on a new-collected dataset with the same evaluation protocol and develop the first standardized benchmark for kinship verification in the wild.
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- NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results35
The 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results establishes a new benchmark for stereo image SR.
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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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