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Recent advances in visual representation learning allowed to build anabundance of powerful off-the-shelf features that are ready-to-use for numerousdownstream tasks. This work aims to assess how well these features preserveinformation about the objects, such as their spatial location, their visualproperties and their relative relationships. We propose to do so by evaluatingthem in the context of visual reasoning, where multiple objects with complexrelationships and different attributes are at play. More specifically, weintroduce a protocol to evaluate visual representations for the task of VisualQuestion Answering. In order to decouple visual feature extraction fromreasoning, we design a specific attention-based reasoning module which istrained on the frozen visual representations to be evaluated, in a spiritsimilar to standard feature evaluations relying on shallow networks. We comparetwo types of visual representations, densely extracted local features andobject-centric ones, against the performances of a perfect image representationusing ground truth. Our main findings are two-fold. First, despite excellentperformances on classical proxy tasks, such representations fall short forsolving complex reasoning problem. Second, object-centric features betterpreserve the critical information necessary to perform visual reasoning. In ourproposed framework we show how to methodologically approach this evaluation.

Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?