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Natural Language Video Description (NLVD) has recently received stronginterest in the Computer Vision, Natural Language Processing (NLP), Multimedia,and Autonomous Robotics communities. The State-of-the-Art (SotA) approachesobtained remarkable results when tested on the benchmark datasets. However,those approaches poorly generalize to new datasets. In addition, none of theexisting works focus on the processing of the input to the NLVD systems, whichis both visual and textual. In this work, it is presented an extensive studydealing with the role of the visual input, evaluated with respect to theoverall NLP performance. This is achieved performing data augmentation of thevisual component, applying common transformations to model camera distortions,noise, lighting, and camera positioning, that are typical in real-worldoperative scenarios. A t-SNE based analysis is proposed to evaluate the effectsof the considered transformations on the overall visual data distribution. Forthis study, it is considered the English subset of Microsoft Research VideoDescription (MSVD) dataset, which is used commonly for NLVD. It was observedthat this dataset contains a relevant amount of syntactic and semantic errors.These errors have been amended manually, and the new version of the dataset(called MSVD-v2) is used in the experimentation. The MSVD-v2 dataset isreleased to help to gain insight into the NLVD problem.

The Role of the Input in Natural Language Video Description