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While seemingly remarkable, it needs a deeper investigation on what knowledge the machine actually learns—does it understand the multi-modal information?
Or it relies on and over-fits to the incidental dataset statistics.
Specifically, the resulting model can ignore the visual information, the question, or both while still doing well on the task.
We thus propose automatic procedures to remedy such design deficiencies.
Time: pm - pm Location: 11th Floor Large Conference Room  Abstract: To generate language, we model what to say, why not also model how listeners will react?
We show how pragmatic inference can be used to both generate and interpret natural language instructions for complex, sequential tasks.
Merely in the past three years, over a dozen datasets have been released, together with many learning-based models that have been narrowing the gap between the humans’ performance and the machines’.
On one popular dataset VQA, the state-of-the-art model achieves 71.4% accuracy, just 17% shy of that by humans.
To this end, we develop a domain adaptation algorithm for Visual QA to facilitate knowledge transfer.
Contact the current seminar organizer, Nanyun (Violet) Peng (npeng at isi dot edu), to schedule a talk.
Time: pm - pm Location: 11th Floor Large Conference Room  Abstract: Visual question answering (Visual QA) requires comprehending and reasoning with both visual and language information, a characteristic ability that AI should strive to achieve.
I will present research that significantly improves performance on two such tasks: answering complex questions over tables, and open-domain factoid question answering.
For answering complex questions, I will present a type-constrained encoder-decoder neural semantic parser that learns to map natural language questions to programs.
Bio: Wei-Lun (Harry) Chao is a Computer Science Ph D candidate at University of Southern California, working with Fei Sha.