Search Results for author:
Found 9 papers, 4 papers with code
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.
Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning
Moreover, this mapping is possible only if the topology of the surface mesh is homotopic to a sphere.
Breaking the general election effect. The impact of the 2020 US presidential election on Chinese economy and counter strategies
This enabled us to obtain detailed quantitative index data of the degree of influence [10][12][14].
Improved deep learning techniques in gravitational-wave data analysis
We point out that CNN models are robust to the variation of the parameter range of the GW waveform.
Deep Image Clustering with Category-Style Representation
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
Reduced-order surrogate models for scalar-tensor gravity in the strong field and applications to binary pulsars and GW170817
We investigate the scalar-tensor gravity of Damour and Esposito-Far\`ese (DEF), which predicts non-trivial phenomena in the nonperturbative strong-field regime for neutron stars (NSs).
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization
Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation.
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers.
Object-Part Attention Model for Fine-grained Image Classification
Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories.
