Vessel Traffic Flow Forecasting (VTFF) using Machine Learning Methods
Descriptions of the implementation of the Vessel Traffic Flow Forecasting (VTFF) model that forecasts the vessel traffic flow within a given region.
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Descriptions of the implementation of the Vessel Traffic Flow Forecasting (VTFF) model that forecasts the vessel traffic flow within a given region.
An improved version of the core parts of the code that implements the Vessel Location Forecasting (VLF) and Vessel Route Forecasting (VRF) methods.
Novel framework for Playable Video Generation that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to...
A holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data. The Neighborhood Contrastive Learning (NCL) framework effectively leverages the local...
PyTorch implementation of Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, a co-occurrence discriminator is used to capture the stru...
A tool to allow Visual Transformers (VTs) to learn spatial relations within an image making the VT training much more robust when training data is scarce. The tool can be used jointly with the standard (supervised) training and it does not depend on speci...
Python implementation of novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an interactive...
A novel unsupervised domain adaptation approach for action recognition from videos, inspired by recent literature on contrastive learning. It comprises a novel two-headed deep architecture that simultaneously adopts cross-entropy and contrastive losses fr...
PyTorch implementation of a Geometry-Contrastive Transformer for Generalized 3D Pose Transfer. The novel GC-Transformer can freely conduct robust pose transfer on LARGE meshes at no cost, which could be a boost to Transformers in 3D fields.
Novel two-stage framework with a new Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one refined pixel-level loss in the second stage. In the first stage, the CrossMLP sub-network learns the latent transformation cues between image ...