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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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- Bi-Level Multi-column Convolutional Neural Networks for Facial Landmark Point Detection4
The coordinates of all landmark and their displacement are simultaneously estimated in Global and Local CNNs, hence global shape constraints are naturally and implicitly imposed to make it very robust to significant variations in pose, expression, occlusion, and illumination.
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- Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images3
A novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment and a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentsation results.
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- AdaHet-MKD: An Adaptive Heterogeneous Multi-teacher Knowledge Distillation for Medical Image Analysis2
An Adaptive Heterogeneous Multi-teacher Knowledge Distillation (AdaHet-MKD) framework for effective knowledge transfer across heterogeneous text-image models and among multiple teacher models is proposed, enabling the student model to operate independently of the teacher model's architecture, which enhances flexibility in teacher-student pairings.
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- Partially Supervised Unpaired Multi-modal Learning for Label-Efficient Medical Image Segmentation2
This paper investigates the use of partially labeled data for label-efficient unpaired multi-modal learning and proposes a novel Decomposed partial class adaptation with snapshot Ensembled Self-Training (DEST) framework for it, which outperforms existing methods significantly.
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- Localizing Anatomical Landmarks in Ocular Images Using Zoom-In Attentive Networks2
A zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images achieves promising performances and outperforms state-of-the-art localization methods.
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- Aligning Contrastive Multiple Clusterings with User Interests1
This work proposes Contrastive Multiple Clusterings (CMClusts), which extends contrastive learning to multiple clustering by elevating traditional instance-level contrast to clustering-level contrast and integrates user expectations or interests by extracting desired features through tailored data augmentations, enabling the model to effectively capture user-relevant clustering features.
- SAC-B:Soft Actor-Critic with Bias for Suppressing Q-valueOverestimation in Off-Policy Reinforcement Learning1
This work introduces Soft Actor-Critic with Bias (SAC-B), a novel framework designed to mitigate Q-value overestimation while preserving the strengths of maximum entropy RL and employing an error-decoupled stabilized architecture that isolates this bias prediction module from the critic networks, thereby eliminating error propagation and enhancing stability.
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- Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory–
A framework for annotation-efficient medical segmentation that leverages sparse supervision from scribbles and points is proposed, which achieves competitive or superior performance compared with state-of-the-art weakly supervised methods, while approaching fully supervised accuracy.
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- Dual-Dynamic In-Context Learning for Medical Image Segmentation–
Dual-DSA (Dual-Dynamic Support Alignment), a reliability-aware visual ICL framework that dynamically integrates supports at the patch and support-set levels, achieves strong full-coverage performance and enables confidence-based rejection to improve retained-case performance.
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- Learning Prediction-aware Prior in Transformer Network for Accurate Spatio-Temporal Video Grounding–
A new Prior-Driven Transformer Network (PDTNet) with predicted temporal boundaries as priors to guide object bounding boxes for improved spatial grounding over time and introduces a prediction-aware Gaussian prior to precise object localization.
- Learning Invariant Discriminative Patterns for Unified Anomaly Detection–
This work proposes a novel UAD framework to learn the invariant discriminative patterns through pre-, in- and post-processing modules, which achieves superior generalization in detecting anomalies across unseen domains, outperforming existing state-of-the-art approaches.
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- STUDY ON THE TRAVEL TIME PREDICTION ALGORITHM IN VRGS–
This paper presents an algorithm capable of predicting link travel time under a variety of conditions through the application of time series theory and methods for vehicle route guidance system.
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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