Commentary on “Challenges and Prospects in Vision and Language Research”

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Target Article:
"Challenges and Prospects in Vision and Language Research", by Kushal Kafle, Robik Shrestha and Christopher Kanan

Abstract:
Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of capabilities that integrate computer vision, reasoning, and natural language understanding.
However, the datasets and evaluation procedures used in these tasks are replete with flaws which allows the vision and language (V&L) algorithms to achieve a good performance without a robust understanding of vision and language. We argue for this position based on several recent studies in V&L literature and our own observations of dataset bias, robustness, and spurious correlations. Finally, we propose that several of these challenges can be mitigated by creation of carefully designed benchmarks.

Keywords: vision and language, deep learning, natural language processing, computer vision, image classification, object detection, entity recognition, sentiment analysis, question-answering, dialog systems, language and computation

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