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Found 16 papers, 0 papers with code
HGNet: Hybrid Generative Network for Zero-shot Domain Adaptation
Domain Adaptation as an important tool aims to explore a generalized model trained on well-annotated source knowledge to address learning issue on target domain with insufficient or even no annotation.
Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition
In addition, a dedicated temporal convolution is applied at each level to learn short-term temporal features, which will be carried over from shallow to deep layers to maximize the leverage of low-level details.
CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation
Speech-driven 3D facial animation technology has been developed for years, but its practical application still lacks expectations.
Embedded Representation Learning Network for Animating Styled Video Portrait
The talking head generation recently attracted considerable attention due to its widespread application prospects, especially for digital avatars and 3D animation design.
GCC: Generative Calibration Clustering
Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space.
Sketch3D: Style-Consistent Guidance for Sketch-to-3D Generation
Recently, image-to-3D approaches have achieved significant results with a natural image as input.
Personalized Semantics Excitation for Federated Image Classification
Federated learning casts a light on the collaboration of distributed local clients with privacy protected to attain a more generic global model.
Few-Shot Video Classification via Representation Fusion and Promotion Learning
This operation maximizes the contribution of discriminative frames to further capture the similarity of support and query samples from the same category.
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning.
Privacy Protected Multi-Domain Collaborative Learning
Unsupervised domain adaptation (UDA) aims to transfer knowledge from one or more well-labeled source domains to improve model performance on the different-yet-related target domain without any annotations.
Adaptive Adversarial Network for Source-Free Domain Adaptation
Unsupervised Domain Adaptation solves knowledge transfer along with the coexistence of well-annotated source domain and unlabeled target instances.
Collaborative Normalization for Unsupervised Domain Adaptation
Batch Normalization (BN) as an important component assists Deep Neural Networks achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches.
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation
Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain.
Structure Preserving Generative Cross-Domain Learning
Unsupervised domain adaptation (UDA) casts a light when dealing with insufficient or no labeled data in the target domain by exploring the well-annotated source knowledge in different distributions.
Bi-Directional Generation for Unsupervised Domain Adaptation
To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
Error Analysis of Generalized Nyström Kernel Regression
Nystr\"{o}m method has been used successfully to improve the computational efficiency of kernel ridge regression (KRR).
