IC^3: Image Captioning by Committee Consensus
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If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to approximate the reference distribution of image captions, however, doing so encourages captions that are viewpoint-impoverished. Such captions often focus on only a subset of the possible details, while ignoring potentially useful information in the scene. In this work, we introduce a simple, yet novel, method: "Image Captioning by Committee Consensus" (IC^3), designed to generate a single caption that captures high-level details from several viewpoints. Notably, humans rate captions produced by IC^3 at least as helpful as baseline SOTA models more than two thirds of the time, and IC^3 captions can improve the performance of SOTA automated recall systems by up to 84 for visual description. Our code is publicly available at https://github.com/DavidMChan/caption-by-committee
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