  Holistic scene understanding includes semantic segmentation, surface normal
estimation, object boundary detection, depth estimation, etc. The key aspect of
this problem is to learn representation effectively, as each subtask builds
upon not only correlated but also distinct attributes. Inspired by
visual-prompt tuning, we propose a Task-Specific Prompts Transformer, dubbed
TSP-Transformer, for holistic scene understanding. It features a vanilla
transformer in the early stage and tasks-specific prompts transformer encoder
in the lateral stage, where tasks-specific prompts are augmented. By doing so,
the transformer layer learns the generic information from the shared parts and
is endowed with task-specific capacity. First, the tasks-specific prompts serve
as induced priors for each task effectively. Moreover, the task-specific
prompts can be seen as switches to favor task-specific representation learning
for different tasks. Extensive experiments on NYUD-v2 and PASCAL-Context show
that our method achieves state-of-the-art performance, validating the
effectiveness of our method for holistic scene understanding. We also provide
our code in the following link https://github.com/tb2-sy/TSP-Transformer.
