Quo Vadis: Is Trajectory Forecastingthe Key Towards Long-Term Multi-Object Tracking?

B.1 QAarchitectureandpre-training QA architecture The QA architecture is based on the Episodic Transformer architecture [6] depicted in Figure 1. QA training data set We train the QA with a mix of 4 tasks: Open-Large, PickUp-Large, PutNextTo-Local, and Sequence-Medium. We use a mix of tasks to push the QA to leverage the compositionality of language. Indeed, the Sequence task is created by putting in sequence two tasks from Open, PickUp, and PutNextTo. Theprobability to keep a question depends on the number of words in common between the goal corresponding to the trajectories and the random goal.

A standard solution to speed up the process is to leverage additional reward signals, shaping it to better guide the learning process.

Weare aware that some nice propertieshavebeen lost, such asExcision Theorem44 does not hold.

Image-based virtual try-on is one of the most promising applications of humancentricimagegeneration duetoitstremendous real-worldpotential.