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We are interested in image manipulation via natural language text -- a taskthat is useful for multiple AI applications but requires complex reasoning overmulti-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning(NSCL), which has been quite effective for the task of Visual QuestionAnswering (VQA), for the task of image manipulation. Our system referred to asNeuroSIM can perform complex multi-hop reasoning over multi-object scenes andonly requires weak supervision in the form of annotated data for VQA. NeuroSIMparses an instruction into a symbolic program, based on a Domain SpecificLanguage (DSL) comprising of object attributes and manipulation operations,that guides its execution. We create a new dataset for the task, and extensiveexperiments demonstrate that NeuroSIM is highly competitive with or beats SOTAbaselines that make use of supervised data for manipulation.

Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach