Answer:
1)They were the key to Rome's military might.
2)They were incredibly efficient.
3)They were expertly engineered.
4)They were easy to navigate.
5)They included a sophisticated network of post houses and roadside inns.
6)They were well-protected and patrolled.
Explanation:
Here is 6 different answers select any 2
Answer:
Yes. Skyrim was conquered by the Nords as we all know, they decimated the native elf populations, won wars, and took their land as a reward.
Explanation:
King George III issued a proclamation<span> that forbade colonial settlement west of the Appalachian Mountains. In so doing, he hoped to placate Native </span>Americans<span> who had sided against him </span>during the<span> recently concluded Seven Years' War.
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The one that best summarizes the Russian economy between 1998 and 2007 was: Its economy was in crisis until the price of oil began to rise.
The communistic regime in Russia has destroyed Russia's economy and killed millions of Russsian Citizen.
In 1998 and 2007, Russia's Government heavily invested in the energy sector and nationalize a lot of Companies in this particular sector of business (mostly oil) and managed to rake in a huge profit when the oil price rises.
Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
About SportCaps :
SportsCap proposes a challenging sports dataset called Sports Motion and Recognition Tasks (SMART) dataset, which contains per-frame action labels, manually annotated pose, and action assessment of various challenging sports video clips from professional referees.
Their approaches :
This is especially prevalent in non-daily action classes like fitness and sports domains. This can be mitigated, for example, by annotating domain-specific datasets , but that requires a considerable amount of manual annotation efforts, financial resources, and 3D annotations can only be obtained in controlled conditions. Therefore, we propose to learn domain-specific pose-sensitive representations from unlabeled videos, which can be fine tuned using only a small labeled dataset. ...
Learn more about Monocular 3 D Human :
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