Traj2traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration Patr: Periodicity-aware trajectory recovery for express system via seq2seq model Road Networks Learning to generate maps from trajectories Multimodal deep learning for robust road attribute detection ...
Cheng et al. [25] developed a multi-task learning model with a hybrid CNN-transformer encoder for simultaneous image segmentation and classification using multimodal MRI image inputs. A U-Net-like encoder-decoder architecture was proposed with an additional transformer unit embedded in the bottom of...
The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential
To evaluate the proposed model MulDIC, we conducted experiments to examine whether the proposed multimodal model, MulDIC, which combines text, image, and code data can improve the performance of issue classification. For the experiments, we selected four projects with many issue reports: VS Code,...
BicycleGAN 1.1k Toward Multimodal Image-to-Image Translation TensorFlow2.0-Examples 1.1k 🙄 Difficult algorithm, simple code. fast-autoaugment 1.1k Official Implementation of 'Fast AutoAugment' in PyTorch. fastai_deeplearn_part1 1.1k Notes for Fastai Deep Learning Course HyperGAN 1.1k Composable GAN...
Attentional hidden vectorS1is then fed into the dense layer to producey1 y1= dense(S1) Target timestep i=2 Alignment scorese2jare computed from the source hidden statehiand target hidden states2using the score function given by e21= score(s2, h1) ...
In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer: 2017. p. 169–177. Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A. Three-Class Mammogram Classification Based on Descriptive CNN Features. BioMed Res Int. 2017;:2017. Domingues I, ...
Consequently, the attention weights for different positions within the window are determined, and the aggregation of values within the window is weighted using these attentional weights. This process produces the feature representation within the window. Its main objective is to tackle the problem of ...
"Learning a recurrent residual fusion network for multimodal matching". In: 2017 IEEE International Conference on Computer Vision (ICCV). 4127–4136. Ma, L., Z. Lu, L. Shang, and H. Li (2015). "Multimodal convolutional neural networks for matching image and sentence". In: Proceedings of ...
BicycleGAN 1.1k Toward Multimodal Image-to-Image Translation TensorFlow2.0-Examples 1.1k 🙄 Difficult algorithm, simple code. fast-autoaugment 1.1k Official Implementation of 'Fast AutoAugment' in PyTorch. fastai_deeplearn_part1 1.1k Notes for Fastai Deep Learning Course HyperGAN 1.1k Composable GAN...