Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
This addresses the issue of biased datasets for researchers in computer vision, though it is incremental as it builds on existing emotion recognition methods.
The paper tackled the problem of dataset bias in visual emotion recognition, which hinders model generalization, and proposed a webly supervised approach using stock images that significantly improved generalization without manual labeling.
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, we propose a webly supervised approach by leveraging a large quantity of stock image data. Our approach uses a simple yet effective curriculum guided training strategy for learning discriminative emotion features. We discover that the models learned using our large scale stock image dataset exhibit significantly better generalization ability than the existing datasets without the manual collection of even a single label. Moreover, visual representation learned using our approach holds a lot of promise across a variety of tasks on different image and video datasets.