The classes on the horizontal axis are sorted based on the performance of the task model without DTL. Dashed line shows the median performance of all classes. The implementation for MSTCN [2] and ASFormer [6] are from existing opensource code provided by corresponding authors. The result is shown in Fig.A1 and Fig.A2. Weanticipatemoreperformance improvement with more general constraints that go beyond knowledge in the annotations in future works.

The table below shows the notations grouped by the modules. This work was done when Ziwei Xu was visiting the Center for Research in Computer Vision. The classes on the horizontal axis are sorted based on the performance of the task model without DTL. Dashed line shows the median performance of all classes. The annotation above (below) the line indicates the averaged improvement for classes ranked at top (bottom) 50% in the baseline performance.

This holiday season, feast on the bounty of food-themed stories NVIDIA Blog readers gobbled up in 2022. Startups in the retail industry -- and particularly in quick-service restaurants -- are using NVIDIA AI and robotics technology to make it easier to order food in drive-thrus, find beverages on store shelves and have meals delivered. They're accelerated by NVIDIA Inception, a program that offers go-to-market support, expertise and technology for cutting-edge startups. For those who prefer eye candy, artists also recreated a ramen restaurant using the NVIDIA Omniverse platform for creating and operating metaverse applications. Toronto startup HuEx is developing a conversational AI assistant to handle order requests at the drive-thru speaker box.