• Corpus ID: 208614779

CLOSURE: Assessing Systematic Generalization of CLEVR Models

@article{Bahdanau2019CLOSUREAS,
  title={CLOSURE: Assessing Systematic Generalization of CLEVR Models},
  author={Dzmitry Bahdanau and Harm de Vries and Timothy J. O'Donnell and Shikhar Murty and Philippe Beaudoin and Yoshua Bengio and Aaron C. Courville},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.05783}
}
The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs. To this end, we test models' understanding of referring… 

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References

SHOWING 1-10 OF 33 REFERENCES

Systematic Generalization: What Is Required and Can It Be Learned?

The findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected, whereas systematic generalization in language understanding may require explicit regularizers or priors.

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

A novel method to systematically construct compositional generalization benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets is introduced, and it is demonstrated how this method can be used to create new compositionality benchmarks on top of the existing SCAN dataset.

Analyzing the Behavior of Visual Question Answering Models

Today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).

C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset

This paper proposes a new setting for Visual Question Answering where the test question-answer pairs are compositionally novel compared to training question- answer pairs, and presents a new compositional split of the VQA v1.0 dataset, which it is called Compositional VZA (C-VQA).

Learning Visual Reasoning Without Strong Priors

This work shows that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate, and probes the model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process.

Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks

Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it’s seen as key to the human capacity for generalization in language. Recent work

Learning to Reason: End-to-End Module Networks for Visual Question Answering

End-to-End Module Networks are proposed, which learn to reason by directly predicting instance-specific network layouts without the aid of a parser, and achieve an error reduction of nearly 50% relative to state-of-theart attentional approaches.

ShapeWorld - A new test methodology for multimodal language understanding

We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is

Neural Module Networks

A procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural "modules" into deep networks for question answering, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.).

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

This paper introduces the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences, and tests the zero-shot generalization capabilities of a variety of recurrent neural networks trained on SCAN with sequence-to-sequence methods.