To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.
We approach this task as a sequence tagging problem, where the goal is to produce <POI name, accessibility label> pairs from unstructured text.
Components orthogonal to the global image representation are then extracted from the local information.
Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks.
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance.
Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city.
While these strategies have effectively dealt with the critical situations of outbreaks, the combination of the pandemic and mobility controls has slowed China's economic growth, resulting in the first quarterly decline of Gross Domestic Product (GDP) since GDP began to be calculated, in 1992.
The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos.