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HSNet: hierarchical semantics network for scene parsing
Scene parsing is one of the fundamental tasks in computer vision. Humans tend to perceive a scene in a hierarchical manner, i.e., first identifying...
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Special perceptual parsing for Chinese landscape painting scene understanding: a semantic segmentation approach
The automatic and precise perceptual parsing of Chinese landscape paintings (CLP) significantly aids in the digitization and recreation of artworks....
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A Novel Optimized Context-Based Deep Architecture for Scene Parsing
Determining the optimal parameter values for a scene parsing network architecture is an important task as a network with optimal parameters produces...
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Deep Learning Technique for Human Parsing: A Survey and Outlook
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly...
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Parsing Objects at a Finer Granularity: A Survey
Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical...
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SSPSNet: a single shot panoptic segmentation network for accurate scene parsing
Panoptic segmentation is a challenging task which aims to provide a comprehensive scene parsing result. Researchers have been devoted to improve its...
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A single-stream adaptive scene layout modeling method for scene recognition
Scene recognition has been the foundation of research in computer vision fields. Because scene images typically are composed of specific regions...
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Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-Wise Pseudo Labeling
The Audio-Visual Video Parsing task aims to identify and temporally localize the events that occur in either or both the audio and visual streams of...
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RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging
Existing state-of-the-art 3D point clouds understanding methods merely perform well in a fully supervised manner. To the best of our knowledge, there...
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Element-Arrangement Context Network for Facade Parsing
Facade parsing aims to decompose a building facade image into semantic regions of the facade objects. Considering each architectural element on a...
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Importance First: Generating Scene Graph of Human Interest
Scene graph aims to faithfully reveal humans’ perception of image content. When humans look at a scene, they usually focus on their interested parts...
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Fine-grained person-based image captioning via advanced spectrum parsing
Recent image captioning models have demonstrated remarkable performance in capturing substantial global semantic information in coarse-grained images...
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RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images
The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images...
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Cross-CBAM: a lightweight network for real-time scene segmentation
Real-time semantic segmentation poses a significant challenge in scene parsing. Despite traditional semantic segmentation networks have made...
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A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model...
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Generating comprehensive scene graphs with integrated multiple attribute detection
Scene graphs present the semantic highlights of the underlying image in directed graph form. Their automated generation is often restricted to...
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A global-local feature adaptive fusion network for image scene classification
Convolutional neural networks (CNN) have been widely used in image scene classification and have achieved remarkable progress. However, because the...
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Automatic Generation of 3D Scene Animation Based on Dynamic Knowledge Graphs and Contextual Encoding
Although novel 3D animation techniques could be boosted by a large variety of deep learning methods, flexible automatic 3D applications (involving...
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Advances in deep concealed scene understanding
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage. The current boom in terms of...
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Single Image Based Three-Dimensional Scene Reconstruction Using Semantic and Geometric Priors
Single image based three-dimensional (3D) scene reconstruction has become an important research topic for computer vision and computer graphics...
