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Found 4 papers, 4 papers with code
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition
Specifically, the framework consists of separate global-local adversarial learning modules that learn domain-invariant global and local features independently.
Cross-Domain Facial Expression Recognition
Model Optimization
+2
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users. However, the DNNs-based models usually suffer from high computational complexity, i. e., consuming very long training time and storing huge amount of trainable parameters.
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.
Ranked #51 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
A Universal Update-pacing Framework For Visual Tracking
Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval.
