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Found 7 papers, 3 papers with code
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skeleton annotations at large scales is difficult.
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching
Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities.
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment
In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images.
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition.
Ranked #1 on
Hand Gesture Recognition
on DHG-14
Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset.
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on.
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core.
