Something is fishy! - How ambiguous language affects generalization of video action recognition networks - Patil, Dhruva Kishor, author; Beveridge, J. Ross, advisor; Krishnaswamy, Nikhil, advisor; Ortega, Francisco R., committee member; Clegg, Benjamin, committee member (Colorado State University. Libraries, 2022)
Modern neural networks designed for video action recognition are able to classify video snippets with high degrees of confidence and accuracy. The success of these models lies in the complex feature representations they learn from the training data, but the limitations of these models are rarely linked on a deeper level to the inconsistent quality of the training data. Although newer and better approaches pride themselves on higher evaluation metrics, this dissertation questions whether these networks are recognizing the peculiarities of dataset labels. A reason for these peculiarities lies in the deviation from standardized data collection and curation protocols that ensure quality labels. Consequently, the models may learn data properties that are irrelevant or even undesirable when trained using only a forced choice technique. One solution for these shortcomings is to reinspect the training data and gain better insights towards designing more efficient algorithms. The Something-Something dataset, a popular dataset for video action recognition, has large semantic overlaps both visually as well as linguistically between different labels provided for each video sample. It can be argued that there are multiple possible interpretations of actions in videos and the restriction of one label per video can limit or even negatively impact the network's ability to generalize to even the dataset's own testing data. To validate this claim, this dissertation introduces a human-in-the-loop procedure to review the legacy labels and relabel the Something-Something validation data. When the new labels thus obtained are used to reassess the performance of video action recognition networks, significant gains of almost 12% and 3% in the top-1 and top-5 accuracies respectively are reported. This hypothesis is further validated by visualizing the layer-wise internals of the networks using Grad-CAM to show that the model focuses on relevant salient regions when predicting an action in a video.
Keywords: human-in-the-loop; video action recognition; Grad-CAM; visualization; Something-Something