id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020888-ov2lzus4	Formal, Thibault	Learning to Rank Images with Cross-Modal Graph Convolutions	2020-03-17		.txt	text/plain	5211	256	55	While most of the current approaches for cross-modal retrieval revolve around learning how to represent text and images in a shared latent space, we take a different direction: we propose to generalize the cross-modal relevance feedback mechanism, a simple yet effective unsupervised method, that relies on standard information retrieval heuristics and the choice of a few hyper-parameters. The model can be understood very simply: similarly to PRF methods in standard information retrieval, the goal is to boost images that are visually similar to top images (from a text point of view), i.e. images that are likely to be relevant to the query but were initially badly ranked (which is likely to happen in the web scenario, where text is crawled from source page and can be very noisy).	./cache/cord-020888-ov2lzus4.txt	./txt/cord-020888-ov2lzus4.txt