Search Results for author:
Found 9 papers, 4 papers with code
Data-free Knowledge Distillation for Fine-grained Visual Categorization
Our approach utilizes an adversarial distillation framework with attention generator, mixed high-order attention distillation, and semantic feature contrast learning.
Data-free Knowledge Distillation
Fine-Grained Visual Categorization
+1
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work.
Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem
Several studies have recently pointed that existing Visual Question Answering (VQA) models heavily suffer from the language prior problem, which refers to capturing superficial statistical correlations between the question type and the answer whereas ignoring the image contents.
Semantic-aware Modular Capsule Routing for Visual Question Answering
Visual Question Answering (VQA) is fundamentally compositional in nature, and many questions are simply answered by decomposing them into modular sub-problems.
Polarized deep diffractive neural network for classification, generation, multiplexing and de-multiplexing of orbital angular momentum modes
6 polarized OAM beams with identical total intensity and 8 cylinder vector beams with different topology charges also have been classified effectively.
Review Polarity-wise Recommender
To be specific, in this model, positive and negative reviews are separately gathered and utilized to model the user-preferred and user-rejected aspects, respectively.
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach
However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning.
Attentive Representation Learning with Adversarial Training for Short Text Clustering
Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model.
A Dirichlet Multinomial Mixture Model-based Approach for Short Text Clustering
Short text clustering has become an increasingly important task with the popularity of social media like Twitter, Google+, and Facebook.
