11 Result(s)
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Chapter and Conference Paper
Contextual Augmentation with Bias Adaptive for Few-Shot Video Object Segmentation
Few-shot video object segmentation (FSVOS) is a challenging task that aims to segment new object classes across query videos with limited annotated support images. Typically, meta learner is the main approach ...
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Chapter and Conference Paper
Dynamic-Static Graph Convolutional Network for Video-Based Facial Expression Recognition
Most of the current methods for video-based facial expression recognition (FER) in the wild are based on deep neural networks with attention mechanism to capture the relationships between frames. However, thes...
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Article
Disassembling Convolutional Segmentation Network
In recent years, the convolutional segmentation network has achieved remarkable performance in the computer vision area. However, training a practicable segmentation network is time- and resource-consuming. In...
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Chapter and Conference Paper
Facial Expression Recognition with Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing
Facial expression is an essential factor in conveying human emotional states and intentions. Although remarkable advancement has been made in facial expression recognition (FER) tasks, challenges due to large ...
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Chapter and Conference Paper
Facial Expression Recognition by Expression-Specific Representation Swap**
In the field of facial expression recognition (FER), various FER systems have been explored to encode expression information from facial representations. Although significant progress has been made towards imp...
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Chapter and Conference Paper
A Location Constrained Dual-Branch Network for Reliable Diagnosis of Jaw Tumors and Cysts
The jaw tumors and cysts are usually painless and asymptomatic, which poses a serious threat to patient life quality. Proper and accurate detection at the early stage will effectively relieve patients’ pain an...
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Chapter and Conference Paper
DEAL: Difficulty-Aware Active Learning for Semantic Segmentation
Active learning aims to address the paucity of labeled data by finding the most informative samples. However, when applying to semantic segmentation, existing methods ignore the segmentation difficulty of diff...
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Chapter and Conference Paper
Towards End-to-End Embroidery Style Generation: A Paired Dataset and Benchmark
Despite the numerous developments in image to image translation, further improvement and applications are limited by monotonous datasets, especially the lack of the datasets with aligned image pairs. In this p...
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Chapter and Conference Paper
Speckle Noise Removal in Ultrasound Images Using a Deep Convolutional Neural Network and a Specially Designed Loss Function
The removal of speckle noise in ultrasound images has been the focus of a number of researches. Meanwhile, deep convolutional neural networks (DCNN) has been proved effective for various computer vision tasks,...
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Article
CU-Net: Component Unmixing Network for Textile Fiber Identification
Image-based nondestructive textile fiber identification is a challenging computer vision problem, that is practically useful in fashion, decoration, and design. Although deep learning now outperforms humans in...
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Chapter and Conference Paper
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an ...
