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.

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Effectiveness included safety, coverage of the reception area, recognition of humans, offering of hors d'oeuvres, and detection of need for refills; entertainment value was determined by popular vote. To fully exploit the talents of our team, drawn from Brandeis's multirobot Interaction Lab and robotic theater troupe Ullanta Performance Robotics, we entered a team of three dramatically interacting robots. At this height, they could neither effectively serve nor avoid being tripped over in the crowd, and our participation in the Find-Life-on-Mars event dictated that we be able to switch software and hardware to turn a Mars exploration team into a domestic service staff in fewer than five minutes. We contrived a simple manipulator able to physically offer snacks at a convenient height by attaching a 3-foot pole to the back of each robot. On top of this pole was hinged a rigid tube with a snack container at one end and a serving bowl at the other (see figure).