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In this paper we introduce visual phrases, complex visual composites like â€œa person riding a horseâ€. Visual phrases often display signi?cantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate signi?cant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector signi?cantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multi-class detection system must decode detector outputs to produce ?nal results; this is usually done with nonmaximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving dif?cult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results.
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