IEEE Trans Pattern Anal Mach Intell - Phrasal Recognition.

Tópicos

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{ can(774) often(719) complex(702) }
{ motion(1329) object(1292) video(1091) }
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{ imag(2830) propos(1344) filter(1198) }
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{ framework(1458) process(801) describ(734) }
{ studi(1410) differ(1259) use(1210) }
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{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
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{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
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{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
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{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ method(2212) result(1239) propos(1039) }

Resumo

In this paper we introduce visual phrases, complex visual composites like "a person riding a horse". Visual phrases often display significantly 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 significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly 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 final results; this is usually done with non-maximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult 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.

Resumo Limpo

paper introduc visual phrase complex visual composit like person ride hors visual phrase often display signific reduc visual complex compar compon object appear object can chang profound particip relat introduc dataset suitabl phrasal recognit use familiar pascal object categori demonstr signific experiment gain result exploit visual phrase show visual phrase detector signific outperform baselin detect compon object reason relat even though visual phrase train set tend smaller object argu multiclass detect system must decod detector output produc final result usual done nonmaximum suppress describ novel decod procedur can account accur local context without solv difficult infer problem show decod procedur outperform state art final show decod combin phrasal object detector produc real improv detector result

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