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Lecture Notes in Computer Science
While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important but long overlooked issue of existing visual emotion benchmarks in the form of dataset biases. We design a series of tests to show and measure how such dataset biases obstruct learning a generalizable emotion recognition model. Based on our analysis, wedoi:10.1007/978-3-030-01216-8_36 fatcat:necdmdtaynf4bfiyvin7yhdefu