To be reliable on rare events is an important requirement for systems based on machine learning. In this work we focus on Visual Question Answering (VQA), where, in spite of recent efforts, datasets remain imbalanced, causing shortcomings of current models: tendencies to overly exploit dataset biases and struggles to generalise to unseen associations of concepts. We focus on a systemic evaluation of model error distributions and address fundamental questions: How is the prediction error distributed? What is the prediction accuracy on infrequent vs. frequent concepts? In this work, we design a new benchmark based on a fine-grained reorganization of the GQA dataset [1], which allows to precisely answer these questions. It introduces distributions shifts in both validation and test splits, which are defined on question groups and are thus tailored to each question. We performed a large-scale study and we experimentally demonstrate that several state-of-the-art VQA models, even those specifically designed for bias reduction, fail to address questions involving infrequent concepts. Furthermore, we show that the high accuracy obtained on the frequent concepts alone is mechanically increasing overall accuracy, covering up the true behavior of current VQA models.

This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest learning methods: i) transfer learning and low-dimensional space embedding allows to reduce the dimensionality of the representations. ii) The visual temporal information is handled by a simple score-per-frame selection process, averaged across time. iii) A simple frame selection mechanism is also proposed to weight the images of a sequence. iv) The fusion of the different modalities is performed at prediction level (late fusion). We also highlight the inherent challenges of the AFEW dataset and the difficulty of model selection with as few as 383 validation sequences. The proposed real-time emotion classifier achieved a state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at the Emotion in the Wild 2018 challenge.