Search Results for author:
Found 5 papers, 4 papers with code
Contrastive-Regularized U-Net for Video Anomaly Detection
We propose to employ a U-Net like structure to model both types of dependencies in a unified structure where the encoder learns global dependencies hierarchically on top of local ones; then the decoder propagates this global information back to the segment level for classification.
ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioning
Recent research that applies Transformer-based architectures to image captioning has resulted in state-of-the-art image captioning performance, capitalising on the success of Transformers on natural language tasks.
End-to-End Supermask Pruning: Learning to Prune Image Captioning Models
With the advancement of deep models, research work on image captioning has led to a remarkable gain in raw performance over the last decade, along with increasing model complexity and computational cost.
Image Captioning with Sparse Recurrent Neural Network
Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance.
COMIC: Towards A Compact Image Captioning Model with Attention
This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks.
