  Zero-shot classification is a promising paradigm to solve an applicable
problem when the training classes and test classes are disjoint. Achieving this
usually needs experts to externalize their domain knowledge by manually
specifying a class-attribute matrix to define which classes have which
attributes. Designing a suitable class-attribute matrix is the key to the
subsequent procedure, but this design process is tedious and trial-and-error
with no guidance. This paper proposes a visual explainable active learning
approach with its design and implementation called semantic navigator to solve
the above problems. This approach promotes human-AI teaming with four actions
(ask, explain, recommend, respond) in each interaction loop. The machine asks
contrastive questions to guide humans in the thinking process of attributes. A
novel visualization called semantic map explains the current status of the
machine. Therefore analysts can better understand why the machine misclassifies
objects. Moreover, the machine recommends the labels of classes for each
attribute to ease the labeling burden. Finally, humans can steer the model by
modifying the labels interactively, and the machine adjusts its
recommendations. The visual explainable active learning approach improves
humans' efficiency of building zero-shot classification models interactively,
compared with the method without guidance. We justify our results with user
studies using the standard benchmarks for zero-shot classification.
