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Works54 from public data
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- Semantic Human Matting164
Semantic Human Matting (SHM) is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks and achieves comparable results with state-of-the-art interactive matting methods.
- Saliency Detection in 360$$^\circ $$ Videos112
This paper proposes to implement the spherical convolution on panorama by stretching and rotating the kernel based on the location of patch to be convolved, and proposes a sequential saliency detection by leveraging a spherical U-Net.
- PPGNet: Learning Point-Pair Graph for Line Segment Detection100
This paper proposes to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods and introduces the PPGNet, a convolutional neural network that directly infers a graph from an image.
- Amodal Segmentation Based on Visible Region Segmentation and Shape Prior91
A framework to mimic the behavior of human and solve the ambiguity in the learning, it firstly estimates a coarse visible mask and a coarse amodal mask, then based on the coarse prediction, the model infers the ammodal mask by concentrating on the visible region and utilizing the shape prior in the memory.
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- Personalized Saliency and Its Prediction60
This work proposes to decompose a personalized saliency map into a universalSaliency map (referred to as PSM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized Saliency.
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- FormantPro as a tool for speech analysis and segmentation35
The alignment of continuous formant trajectories enabled by FormantPro provides evidence that the temporal scopes of consonants and vowels are very different from those based on conventional views, and that acoustic and articulatory boundaries of segments are fundamentally similar.
- Layout-Guided Novel View Synthesis from a Single Indoor Panorama32
This paper makes the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration and uses Convolutional Neural Networks to extract the deep features and estimate the depth map from the source-view image.
- Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN27
This study produces the first database of personalized saliency maps (PSMs) based on universal saliency map (USM) shared by different participants and adopts a multitask CNN framework to estimate the discrepancy between PSM and USM.
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- FmH2ST: foundation model-based spatial transcriptomics generation from histological images17
FmH2ST can denoise the raw gene expressions, reveal cancer spatial heterogeneity and gene co-expression patterns, and support the inference of gene regulatory networks, and is effective for predicting ST, with potential applications in clinical diagnostics and personalized treatment.
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- SUNNet: A novel framework for simultaneous human parsing and pose estimation9
A novel Separation-and-UnioN Network (SUNNet) for simultaneous human parsing and pose estimation and a novel Gated Feature Fusion (GFF) block is designed to automatically decide whether to use or skip the priors from the reconstructed 3D human body.
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- ResNeRF: Geometry-Guided Residual Neural Radiance Field for Indoor Scene Novel View Synthesis6
The ResNeRF, a novel geometry-guided two-stage framework for indoor scene novel view synthesis, is represented and it is proposed to characterize the density distribution of the scene based on a base density estimated from scene geometry and a residual density parameterized by the geometry.
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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 2026-10-12. 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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