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Works352 from public data
- Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images851
A Stacked Sparse Autoencoder, an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer and out-performed nine other state of the art nuclear detection strategies.
- Fast Visual Tracking via Dense Spatio-temporal Context Learning705
A novel explicit scale adaptation scheme is proposed, able to deal with target scale variations efficiently and effectively, and the Fast Fourier Transform is adopted for fast learning and detection in this work, which only needs 4 FFT operations.
- Classification of Hyperspectral and LiDAR Data Using Coupled CNNs463
An efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs) designed to learn spectral–spatial features from hyperspectrals and elevation information from LiDAR data is proposed.
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- Hyperspectral Image Classification With Attention-Aided CNNs340
An attention-aided CNN model based on the traditional CNN model that incorporates attention modules to aid networks that focus on more discriminative channels or positions for spectral and spatial classifications of hyperspectral images is proposed.
- Robust Lightweight Facial Expression Recognition Network with Label Distribution Training326
This paper presents an efficiently robust facial expression recognition (FER) network, named EfficientFace, which holds much fewer parameters but more robust to the FER in the wild, and introduces a simple but efficient label distribution learning (LDL) method as a novel training strategy.
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- Former-DFER: Dynamic Facial Expression Recognition Transformer246
The heatmaps of the leaned facial features demonstrate that the proposed Former-DFER is capable of handling the issues such as occlusion, non-frontal pose, and head motion, and the visualization of the feature distribution shows that the propose method can learn more discriminative facial features.
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- Spatio-temporal convolutional features with nested LSTM for facial expression recognition161
A novel end-to-end architecture termed Spatio-Temporal Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level appearance features and temporal dynamics of facial expressions in a joint fashion is proposed.
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- Ensemble learning for independent component analysis149
An enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems of unstable ICA classifier and small sample size problem is proposed.
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- A Variational Approach to Simultaneous Image Segmentation and Bias Correction119
An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved and the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost.
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 2026-10-14. 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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