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Found 7 papers, 3 papers with code
Pose Guided Attention for Multi-label Fashion Image Classification
We propose a compact framework with guided attention for multi-label classification in the fashion domain.
Subspace Segmentation by Successive Approximations: A Method for Low-Rank and High-Rank Data with Missing Entries
We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces.
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion.
Discriminative Optimization: Theory and Applications to Computer Vision Problems
This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima.
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.
Understanding People Flow in Transportation Hubs
Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management.
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective.
