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The use of explicit object detectors as an intermediate step to imagecaptioning - which used to constitute an essential stage in early work - isoften bypassed in the currently dominant end-to-end approaches, where thelanguage model is conditioned directly on a mid-level image embedding. We arguethat explicit detections provide rich semantic information, and can thus beused as an interpretable representation to better understand why end-to-endimage captioning systems work well. We provide an in-depth analysis ofend-to-end image captioning by exploring a variety of cues that can be derivedfrom such object detections. Our study reveals that end-to-end image captioningsystems rely on matching image representations to generate captions, and thatencoding the frequency, size and position of objects are complementary and allplay a role in forming a good image representation. It also reveals thatdifferent object categories contribute in different ways towards imagecaptioning.

Object Counts! Bringing Explicit Detections Back into Image Captioning