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Found 7 papers, 0 papers with code
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation
We tackle this problem by proposing an off-policy RL learning algorithm where a behaviour policy represented by GRUs performs the sampling.
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs
Furthermore, to enable the ParaCNN to model paragraph comprehensively, we also propose an adversarial twin net training scheme.
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning
Then the proposed HorNet can learn the visual and language representation from both the images and captions jointly, and thus enhance the performance of person re-ID.
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.
