35 research outputs found
Answer-Type Prediction for Visual Question Answering
Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach’s key insight is that we can predict the form of the answer from the question. We formulate our solution in a Bayesian framework. When our approach is combined with a discriminative model, the combined model achieves state-of-the-art results on four benchmark datasets for open-ended VQA: DAQUAR, COCO-QA, The VQA Dataset, and Visual7W
TallyQA: Answering Complex Counting Questions
Most counting questions in visual question answering (VQA) datasets are
simple and require no more than object detection. Here, we study algorithms for
complex counting questions that involve relationships between objects,
attribute identification, reasoning, and more. To do this, we created TallyQA,
the world's largest dataset for open-ended counting. We propose a new algorithm
for counting that uses relation networks with region proposals. Our method lets
relation networks be efficiently used with high-resolution imagery. It yields
state-of-the-art results compared to baseline and recent systems on both
TallyQA and the HowMany-QA benchmark.Comment: To appear in AAAI 2019 ( To download the dataset please go to
http://www.manojacharya.com/
Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization
A deeper understanding of video activities extends beyond recognition of
underlying concepts such as actions and objects: constructing deep semantic
representations requires reasoning about the semantic relationships among these
concepts, often beyond what is directly observed in the data. To this end, we
propose an energy minimization framework that leverages large-scale commonsense
knowledge bases, such as ConceptNet, to provide contextual cues to establish
semantic relationships among entities directly hypothesized from video signal.
We mathematically express this using the language of Grenander's canonical
pattern generator theory. We show that the use of prior encoded commonsense
knowledge alleviate the need for large annotated training datasets and help
tackle imbalance in training through prior knowledge. Using three different
publicly available datasets - Charades, Microsoft Visual Description Corpus and
Breakfast Actions datasets, we show that the proposed model can generate video
interpretations whose quality is better than those reported by state-of-the-art
approaches, which have substantial training needs. Through extensive
experiments, we show that the use of commonsense knowledge from ConceptNet
allows the proposed approach to handle various challenges such as training data
imbalance, weak features, and complex semantic relationships and visual scenes.Comment: Accepted to WACV 201
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Out-of-distribution (OOD) testing is increasingly popular for evaluating a
machine learning system's ability to generalize beyond the biases of a training
set. OOD benchmarks are designed to present a different joint distribution of
data and labels between training and test time. VQA-CP has become the standard
OOD benchmark for visual question answering, but we discovered three troubling
practices in its current use. First, most published methods rely on explicit
knowledge of the construction of the OOD splits. They often rely on
``inverting'' the distribution of labels, e.g. answering mostly 'yes' when the
common training answer is 'no'. Second, the OOD test set is used for model
selection. Third, a model's in-domain performance is assessed after retraining
it on in-domain splits (VQA v2) that exhibit a more balanced distribution of
labels. These three practices defeat the objective of evaluating
generalization, and put into question the value of methods specifically
designed for this dataset. We show that embarrassingly-simple methods,
including one that generates answers at random, surpass the state of the art on
some question types. We provide short- and long-term solutions to avoid these
pitfalls and realize the benefits of OOD evaluation