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Found 4 papers, 1 papers with code
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction
In this paper, we propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction, to cope with the challenge of predicting motions shaped by multi-scale interactions.
Short-term and long-term memory self-attention network for segmentation of tumours in 3D medical images
Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes.
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory
Based on the drastic changes we found of the generalization error bound under different adversarial attacks and different training states, we proposed an adaptive training method which can greatly improve the image manipulation ability of multi-scale GANs.
IoU-uniform R-CNN: Breaking Through the Limitations of RPN
Region Proposal Network (RPN) is the cornerstone of two-stage object detectors, it generates a sparse set of object proposals and alleviates the extrem foregroundbackground class imbalance problem during training.
