In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper net- work architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.

A: Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles.
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Q: As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative.   For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot's behavior. These approaches, however, fail to consider the mental workload needed to understand the received explanation. In other words, the human teammate is expected to understand any explanation provided, often before the task execution, no matter how much information is presented in the explanation.
 In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reducing the mental workload of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented along with three different implementations satisfying different online properties. We base our explanation generation method on a model reconciliation setting introduced in our prior work. Our approaches are evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with ten different problems across two IPC domains.


A: We introduce online explanation to consider the cognitive requirement of the human for understanding the generated explanation by the agent.
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Q:   We introduce a model that learns to convert simple hand drawings
  into graphics programs written in a subset of \LaTeX.~ The model
  combines techniques from deep learning and program synthesis.   We
  learn a convolutional neural network that proposes plausible drawing
  primitives that explain an image. These drawing primitives are like
  a trace of the set of primitive commands issued by a graphics
  program. We learn a model that uses program synthesis techniques to
  recover a graphics program from that trace. These programs have
  constructs like variable bindings, iterative loops, or simple kinds
  of conditionals. With a graphics program in hand, we can correct
  errors made by the deep network and extrapolate drawings.   Taken
  together these results are a step towards agents that induce useful,
  human-readable programs from perceptual input.

A:
Learn to convert a hand drawn sketch into a high-level program
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