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Found 5 papers, 0 papers with code
Attentive Prototype Few-shot Learning with Capsule Network-based Embedding
Few-shot learning, namely recognizing novel categories with a very small amount of training examples, is a challenging area of machine learning research.
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization
Automatically generating the descriptions of an image, i. e., image captioning, is an important and fundamental topic in artificial intelligence, which bridges the gap between computer vision and natural language processing.
Hierarchical Multi-scale Attention Networks for Action Recognition
Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.
Traffic scene recognition based on deep cnn and vlad spatial pyramids
In this paper, we attempted to solve the traffic scene recognition problem by combining the features representational capabilities of CNN with the VLAD encoding scheme.
CHAM: action recognition using convolutional hierarchical attention model
This paper presents improvements to the soft attention model by combining a convolutional LSTM with a hierarchical system architecture to recognize action categories in videos.
