89 research outputs found
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for
evaluating the abilities and shortcomings of current systems for image
understanding. Many of the recently proposed VQA systems include attention or
memory mechanisms designed to support "reasoning". For multiple-choice VQA,
nearly all of these systems train a multi-class classifier on image and
question features to predict an answer. This paper questions the value of these
common practices and develops a simple alternative model based on binary
classification. Instead of treating answers as competing choices, our model
receives the answer as input and predicts whether or not an
image-question-answer triplet is correct. We evaluate our model on the Visual7W
Telling and the VQA Real Multiple Choice tasks, and find that even simple
versions of our model perform competitively. Our best model achieves
state-of-the-art performance on the Visual7W Telling task and compares
surprisingly well with the most complex systems proposed for the VQA Real
Multiple Choice task. We explore variants of the model and study its
transferability between both datasets. We also present an error analysis of our
model that suggests a key problem of current VQA systems lies in the lack of
visual grounding of concepts that occur in the questions and answers. Overall,
our results suggest that the performance of current VQA systems is not
significantly better than that of systems designed to exploit dataset biases.Comment: European Conference on Computer Visio
SIMCO: SIMilarity-based object COunting
We present SIMCO, the first agnostic multi-class object counting approach.
SIMCO starts by detecting foreground objects through a novel Mask RCNN-based
architecture trained beforehand (just once) on a brand-new synthetic 2D shape
dataset, InShape; the idea is to highlight every object resembling a primitive
2D shape (circle, square, rectangle, etc.). Each object detected is described
by a low-dimensional embedding, obtained from a novel similarity-based head
branch; this latter implements a triplet loss, encouraging similar objects
(same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this
embedding for clustering, so that different types of objects can emerge and be
counted, making SIMCO the very first multi-class unsupervised counter.
Experiments show that SIMCO provides state-of-the-art scores on counting
benchmarks and that it can also help in many challenging image understanding
tasks
Structure Learning for Neural Module Networks
Neural Module Networks, originally proposed for the task of visual question
answering, are a class of neural network architectures that involve
human-specified neural modules, each designed for a specific form of reasoning.
In current formulations of such networks only the parameters of the neural
modules and/or the order of their execution is learned. In this work, we
further expand this approach and also learn the underlying internal structure
of modules in terms of the ordering and combination of simple and elementary
arithmetic operators. Our results show that one is indeed able to
simultaneously learn both internal module structure and module sequencing
without extra supervisory signals for module execution sequencing. With this
approach, we report performance comparable to models using hand-designed
modules
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