Search Results for author:
Found 16 papers, 2 papers with code
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories.
Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering.
Traffic Sign Interpretation in Real Road Scene
Following the above issues, we propose a traffic sign interpretation (TSI) task, which aims to interpret global semantic interrelated traffic signs (e. g.,~driving instruction-related texts, symbols, and guide panels) into a natural language for providing accurate instruction support to autonomous or assistant driving.
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image.
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e. g., security.
Fully Self-Supervised Depth Estimation from Defocus Clue
We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions, thus closing the gap between the theoretical success of DFD works and their applications in the real world.
Adaptive Shrink-Mask for Text Detection
It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results.
MT: Multi-Perspective Feature Learning Network for Scene Text Detection
Text detection, the key technology for understanding scene text, has become an attractive research topic.
Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering.
Auto-weighted Multi-view Feature Selection with Graph Optimization
In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning.
BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network
Specifically, a new text representation strategy is proposed to represent text contours from a top-down perspective, which can fit highly curved text contours effectively.
Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning
Graph-based semi-supervised learning methods, which deal well with the situation of limited labeled data, have shown dominant performance in practical applications.
Entropy Minimizing Matrix Factorization
Considering that the outliers are usually much less than the normal samples, a new entropy loss function is established for matrix factorization, which minimizes the entropy of the residue distribution and allows a few samples to have large approximation errors.
Feature Weighted Non-negative Matrix Factorization
Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis.
Multi-channel Deep Supervision for Crowd Counting
To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth.
CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection
Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition.
