ArticleCognitive neurodynamics2021
End-to-end face parsing via interlinked convolutional neural networks.
Article in Cognitive neurodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Affective EEG-based cross-session person identification using hierarchical graph embedding.Cognitive neurodynamics · 2024Article
- DRF-DRC: dynamic receptive field and dense residual connections for model compression.Cognitive neurodynamics · 2023Article
- Image denoising and segmentation model construction based on IWOA-PCNN.Scientific reports · 2023Article
- Facial expression recognition on partially occluded faces using component based ensemble stacked CNN.Cognitive neurodynamics · 2023Article
- Real-time echocardiography image analysis and quantification of cardiac indices.Medical image analysis · 2022Article
- Trilateral Attention Network for Real-Time Cardiac Region Segmentation.IEEE access : practical innovations, open solutions · 2021Article
- High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction.Sensors (Basel, Switzerland) · 2020Article
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4 authors.
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Abstract
Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model for face parsing. However, the original iCNN was trained separately in two stages, limiting its performance. To solve this problem, we introduce a simple, end-to-end face parsing framework: STN-aided iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to these two advantages, our approach significantly improves the accuracy of the original model. Our model achieved competitive performance on the Helen Dataset, the standard face parsing dataset. It also achieved superior performance on CelebAMask-HQ dataset, proving its good generalization. Our code has been released at https://github.com/aod321/STN-iCNN.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.