FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2998515736> ?p ?o ?g. }

• W2998515736 abstract "Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks are currently reaching a plateau, any extra knowledge, including context, should be leveraged in order to seek significant leaps in these performances. In the particular scenario of kernel machines, context-aware kernel design aims at learning positive semi-definite similarity functions which return high values not only when data share similar contents, but also similar structures (a.k.a contexts). However, the use of context in kernel design has not been fully explored; indeed, context in these solutions is handcrafted instead of being learned. In this paper, we introduce a novel deep network architecture that learns context in kernel design. This architecture is fully determined by the solution of an objective function mixing a content term that captures the intrinsic similarity between data, a context criterion which models their structure and a regularization term that helps designing smooth kernel network representations. The solution of this objective function defines a particular deep network architecture whose parameters correspond to different variants of learned contexts including layerwise, stationary and classwise; larger values of these parameters correspond to the most influencing contextual relationships between data. Extensive experiments conducted on the challenging ImageCLEF Photo Annotation and Corel5k benchmarks show that our deep context networks are highly effective for image classification and the learned contexts further enhance the performance of image annotation." @default.
• W2998515736 created "2020-01-10" @default.
• W2998515736 creator A5017257056 @default.
• W2998515736 creator A5029896607 @default.
• W2998515736 date "2019-12-29" @default.
• W2998515736 modified "2023-09-23" @default.
• W2998515736 title "Deep Context-Aware Kernel Networks" @default.
• W2998515736 cites W1026270304 @default.
• W2998515736 cites W1510073064 @default.
• W2998515736 cites W1515999713 @default.
• W2998515736 cites W1591394246 @default.
• W2998515736 cites W1601437336 @default.
• W2998515736 cites W1666447063 @default.
• W2998515736 cites W167790647 @default.
• W2998515736 cites W1877469910 @default.
• W2998515736 cites W1992208818 @default.
• W2998515736 cites W2005347316 @default.
• W2998515736 cites W2011589513 @default.
• W2998515736 cites W2020596545 @default.
• W2998515736 cites W2031823405 @default.
• W2998515736 cites W2035976912 @default.
• W2998515736 cites W2036681051 @default.
• W2998515736 cites W2037407504 @default.
• W2998515736 cites W2039182213 @default.
• W2998515736 cites W2046589280 @default.
• W2998515736 cites W2046665279 @default.
• W2998515736 cites W2046991152 @default.
• W2998515736 cites W2049033299 @default.
• W2998515736 cites W2054103873 @default.
• W2998515736 cites W2057175746 @default.
• W2998515736 cites W2069797086 @default.
• W2998515736 cites W2070244236 @default.
• W2998515736 cites W2089902864 @default.
• W2998515736 cites W2095844239 @default.
• W2998515736 cites W2097117768 @default.
• W2998515736 cites W2102605133 @default.
• W2998515736 cites W2104529636 @default.
• W2998515736 cites W2109235804 @default.
• W2998515736 cites W2111073379 @default.
• W2998515736 cites W2112562896 @default.
• W2998515736 cites W2112796928 @default.
• W2998515736 cites W2115708871 @default.
• W2998515736 cites W2117539524 @default.
• W2998515736 cites W2118679727 @default.
• W2998515736 cites W2123872146 @default.
• W2998515736 cites W2124386111 @default.
• W2998515736 cites W2127069950 @default.
• W2998515736 cites W2127411609 @default.
• W2998515736 cites W2134380836 @default.
• W2998515736 cites W2136940668 @default.
• W2998515736 cites W2136951832 @default.
• W2998515736 cites W2137055149 @default.
• W2998515736 cites W2137471889 @default.
• W2998515736 cites W2145295623 @default.
• W2998515736 cites W2145908571 @default.
• W2998515736 cites W2146024151 @default.
• W2998515736 cites W2150341604 @default.
• W2998515736 cites W2153635508 @default.
• W2998515736 cites W2155867588 @default.
• W2998515736 cites W2162867699 @default.
• W2998515736 cites W2163605009 @default.
• W2998515736 cites W2169625877 @default.
• W2998515736 cites W2172231696 @default.
• W2998515736 cites W2194775991 @default.
• W2998515736 cites W2265864126 @default.
• W2998515736 cites W2294422256 @default.
• W2998515736 cites W2395459784 @default.
• W2998515736 cites W2399164823 @default.
• W2998515736 cites W2435338979 @default.
• W2998515736 cites W2499468060 @default.
• W2998515736 cites W2536305071 @default.
• W2998515736 cites W2557283755 @default.
• W2998515736 cites W2587063199 @default.
• W2998515736 cites W2591766052 @default.
• W2998515736 cites W2620572990 @default.
• W2998515736 cites W2769171608 @default.
• W2998515736 cites W2790584795 @default.
• W2998515736 cites W2793406320 @default.
• W2998515736 cites W2794084401 @default.
• W2998515736 cites W2799873835 @default.
• W2998515736 cites W2802644759 @default.
• W2998515736 cites W28864682 @default.
• W2998515736 cites W2895163674 @default.
• W2998515736 cites W2895703551 @default.
• W2998515736 cites W2903909270 @default.
• W2998515736 cites W2921770506 @default.
• W2998515736 cites W2962751169 @default.
• W2998515736 cites W2962967183 @default.
• W2998515736 cites W2963014573 @default.
• W2998515736 cites W2963173190 @default.
• W2998515736 cites W2964163358 @default.
• W2998515736 cites W993240969 @default.
• W2998515736 doi "https://doi.org/10.48550/arxiv.1912.12735" @default.
• W2998515736 hasPublicationYear "2019" @default.
• W2998515736 type Work @default.
• W2998515736 sameAs 2998515736 @default.
• W2998515736 citedByCount "0" @default.
• W2998515736 crossrefType "posted-content" @default.
• W2998515736 hasAuthorship W2998515736A5017257056 @default.
• W2998515736 hasAuthorship W2998515736A5029896607 @default.

• next