104 research outputs found
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/
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
Measuring Catastrophic Forgetting in Neural Networks
Deep neural networks are used in many state-of-the-art systems for machine
perception. Once a network is trained to do a specific task, e.g., bird
classification, it cannot easily be trained to do new tasks, e.g.,
incrementally learning to recognize additional bird species or learning an
entirely different task such as flower recognition. When new tasks are added,
typical deep neural networks are prone to catastrophically forgetting previous
tasks. Networks that are capable of assimilating new information incrementally,
much like how humans form new memories over time, will be more efficient than
re-training the model from scratch each time a new task needs to be learned.
There have been multiple attempts to develop schemes that mitigate catastrophic
forgetting, but these methods have not been directly compared, the tests used
to evaluate them vary considerably, and these methods have only been evaluated
on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics
and benchmarks for directly comparing five different mechanisms designed to
mitigate catastrophic forgetting in neural networks: regularization,
ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on
real-world images and sounds show that the mechanism(s) that are critical for
optimal performance vary based on the incremental training paradigm and type of
data being used, but they all demonstrate that the catastrophic forgetting
problem has yet to be solved.Comment: To appear in AAAI 201
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