Study lead author Sjoukje Philip of the Royal Netherlands Meteorological Institute said in a briefing that a weather event this extreme “would have been almost impossible in the past, colder climate,” adding: “We will see more intense and more frequent heat waves in the future as global warming continues.”īecause the analysis released Friday was one of the quickest ever - the heat still hasn’t subsided much - the study by World Weather Attribution is not peer reviewed, which is the gold standard for science. The four countries experienced temperatures as high as 36.9 degrees Celsius (98.4 degrees Fahrenheit) to 41 degrees Celsius (105.8 degrees Fahrenheit) degrees. State-of-the-art approaches.MADRID (AP) - Record-breaking April temperatures in Spain, Portugal and northern Africa were made 100 times more likely by human-caused climate change, a new flash study found, and would have been almost impossible in the past.Ī group of international scientists did a rapid computer and statistical analysis of a late-April heat wave that stretched across the Iberian peninsula into Algeria and Morocco. Two public multi-modal human activity recognition datasets with various Transformer module, to produce the results. Which consists of a lite version of the multi-modal spatial-temporal Learned representation from the teacher model to a simpler DMFT student model,
Then the knowledge distillation method is applied to transfer the Temporal mid-fusion module is also proposed to further fuse the temporalįeatures. Transformer module that extracts the salient spatial-temporal features. Then the DMFT teacher model applies an attentive multi-modal spatial-temporal
Resolve the issues, a knowledge distillation-based Multi-modal Mid-FusionĪpproach, DMFT, is proposed to conduct informative feature extraction andįusion to resolve the Multi-modal Human Activity Recognition task efficiently.ĭMFT first encodes the multi-modal input data into a unified representation. Multi-modal approach for edge deployment is another problem yet to resolve. Multi-modal spatial-temporal features and better fusing complementary Methods have shown promising results, their potential in extracting salient Information to build models that can generalize well. Sensors, Multi-modal Human Activity Recognition could utilize the complementary Robust and effective enough for real-world deployment. Quality and require modality-specific feature engineering, thus not being Uni-modal approaches have been extensively studied, they suffer from data Download a PDF of the paper titled Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition, by Jingcheng Li and 3 other authors Download PDF Abstract: Human Activity Recognition is an important task in many human-computerĬollaborative scenarios, whilst having various practical applications.
0 kommentar(er)
