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We consider the task of visual net surgery, in which a CNN can bereconfigured without extra data to recognize novel concepts that may be omittedfrom the training set. While most prior work make use of linguistic cues forsuch "zero-shot" learning, we do so by using a pictorial languagerepresentation of the training set, implicitly learned by a CNN, to generalizeto new classes. To this end, we introduce a set of visualization techniquesthat better reveal the activation patterns and relations between groups of CNNfilters. We next demonstrate that knowledge of pictorial languages can be usedto rewire certain CNN neurons into a part model, which we call a pictoriallanguage classifier. We demonstrate the robustness of simple PLCs by applyingthem in a weakly supervised manner: labeling unlabeled concepts for visualclasses present in the training data. Specifically we show that a PLC built ontop of a CNN trained for ImageNet classification can localize humans in Graz-02and determine the pose of birds in PASCAL-VOC without extra labeled data oradditional training. We then apply PLCs in an interactive zero-shot manner,demonstrating that pictorial languages are expressive enough to detect a set ofvisual classes in MS-COCO that never appear in the ImageNet training set.

Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery