Learning to find good correspondences
|
|
|
- Elżbieta Urbańska
- 5 lat temu
- Przeglądów:
Transkrypt
1 Learning to find good correspondences K.M. Yi, E. Trulls, Y. Ono, V. Lepetit, M. Salzmann, P. Fua CVPR 2018 (Salt Lake City, UT, USA)
2 Local features matter Keypoints provide us with a robust way to match points across images.
3 Local features matter Keypoints: location (x, y), orientation, scale. Descriptors: histograms of gradient orientations. Source:
4 Local features matter Matching large numbers of local features allows us to recover structure! Source: OpenIMAJ (
5 {<latexit sha1_base64="audfbnkxz8fqqkqu0b14/dsqtaq=">aaab6xicdvbns8naej34wetx1aoxxsj4ckknbb0vvxisyj+gdwwz3brln5uwuxfk6d/w4kerr/4jb/4bn20ffx0w8hhvhpl5qckz0o7zya2srq1vbba2its7u3v7pypdtoptswilxdyw3qarypmglc00p91euhwfnhacyvxud+6pvcwwd3qaud/ci8fcrra20m0/g5tkjn1rr1a8knjsx6m5ftcnlzp37ihxkdnkserzuhrvd2osrlrowrfspddjtj9hqrnhdfbsp4ommezwipymftiiys/ml87qqvggkiylkahrxp0+kefiqwkumm4i67h67exix14v1whdz5hiuk0fwswku450jpk30zbjsjsfgokjzozwrmzyyqjnoeutwten6h/srtiuy7s3xrlxuyyjamdwamfgqg0aca1naagbeb7gcz6tifvovvivi9yvazlzbd9gvx0c+5onpw==</latexit> <latexit sha1_base64="audfbnkxz8fqqkqu0b14/dsqtaq=">aaab6xicdvbns8naej34wetx1aoxxsj4ckknbb0vvxisyj+gdwwz3brln5uwuxfk6d/w4kerr/4jb/4bn20ffx0w8hhvhpl5qckz0o7zya2srq1vbba2its7u3v7pypdtoptswilxdyw3qarypmglc00p91euhwfnhacyvxud+6pvcwwd3qaud/ci8fcrra20m0/g5tkjn1rr1a8knjsx6m5ftcnlzp37ihxkdnkserzuhrvd2osrlrowrfspddjtj9hqrnhdfbsp4ommezwipymftiiys/ml87qqvggkiylkahrxp0+kefiqwkumm4i67h67exix14v1whdz5hiuk0fwswku450jpk30zbjsjsfgokjzozwrmzyyqjnoeutwten6h/srtiuy7s3xrlxuyyjamdwamfgqg0aca1naagbeb7gcz6tifvovvivi9yvazlzbd9gvx0c+5onpw==</latexit> <latexit sha1_base64="audfbnkxz8fqqkqu0b14/dsqtaq=">aaab6xicdvbns8naej34wetx1aoxxsj4ckknbb0vvxisyj+gdwwz3brln5uwuxfk6d/w4kerr/4jb/4bn20ffx0w8hhvhpl5qckz0o7zya2srq1vbba2its7u3v7pypdtoptswilxdyw3qarypmglc00p91euhwfnhacyvxud+6pvcwwd3qaud/ci8fcrra20m0/g5tkjn1rr1a8knjsx6m5ftcnlzp37ihxkdnkserzuhrvd2osrlrowrfspddjtj9hqrnhdfbsp4ommezwipymftiiys/ml87qqvggkiylkahrxp0+kefiqwkumm4i67h67exix14v1whdz5hiuk0fwswku450jpk30zbjsjsfgokjzozwrmzyyqjnoeutwten6h/srtiuy7s3xrlxuyyjamdwamfgqg0aca1naagbeb7gcz6tifvovvivi9yvazlzbd9gvx0c+5onpw==</latexit> <latexit sha1_base64="audfbnkxz8fqqkqu0b14/dsqtaq=">aaab6xicdvbns8naej34wetx1aoxxsj4ckknbb0vvxisyj+gdwwz3brln5uwuxfk6d/w4kerr/4jb/4bn20ffx0w8hhvhpl5qckz0o7zya2srq1vbba2its7u3v7pypdtoptswilxdyw3qarypmglc00p91euhwfnhacyvxud+6pvcwwd3qaud/ci8fcrra20m0/g5tkjn1rr1a8knjsx6m5ftcnlzp37ihxkdnkserzuhrvd2osrlrowrfspddjtj9hqrnhdfbsp4ommezwipymftiiys/ml87qqvggkiylkahrxp0+kefiqwkumm4i67h67exix14v1whdz5hiuk0fwswku450jpk30zbjsjsfgokjzozwrmzyyqjnoeutwten6h/srtiuy7s3xrlxuyyjamdwamfgqg0aca1naagbeb7gcz6tifvovvivi9yvazlzbd9gvx0c+5onpw==</latexit> Why should I care? SIFT (journal paper) (conference paper) 64k citations!
6 Applications: panorama stitching Source:
7 Applications: 3D reconstruction
8 Applications: camera pose retrieval Source: S.M. Yoon et al, Hierarchical image representation using 3D camera geometry for content-based image retrieval, EAAI 2014.
9 What about Deep Learning? Recent works by the Computer Vision lab at EPFL: TILDE: A Temporally Invariant Learned DEtector (CVPR 15). Discriminative learning of descriptors (ICCV 15). Learning to assign orientations to feature points (CVPR 16). LIFT: Learned Invariant Feature Transform (ECCV 16). Learning to find good correspondences (CVPR 18). LF-Net: Learning local features from images (arxiv 18).
10 What about Deep Learning? Recent works by the Computer Vision lab at EPFL: TILDE: A Temporally Invariant Learned DEtector (CVPR 15). Discriminative learning of descriptors (ICCV 15). Learning to assign orientations to feature points (CVPR 16). LIFT: Learned Invariant Feature Transform (ECCV 16). Learning to find good correspondences (CVPR 18). LF-Net: Learning local features from images (arxiv 18).
11 Matching keypoints (c) Retrieve pose (a) Find putative matches (b) Find inliers (e.g. RANSAC) Fischler & Bolles, Random Sample Consensus. Comm. ACM, 1981
12 Dense matching with CNNs Current focus of research: Zamir et al, ECCV 16. SfM-Net, arxiv 17. DeMoN, CVPR 17. Lowe et al, CVPR 17. Focus: video, small displacements. General case (wide baselines) remains unsolved.
13 Where s the challenge?
14 RANSAC: not always enough
15 Geometry to the rescue
16 Geometry to the rescue A geometrically-aware deep network. Input: correspondences. Output: one weight for each. We simultaneously learn to: Perform outlier rejection. Regress to the essential matrix.
17 Computing the Essential matrix Closed form solution: 8-point algorithm Nx9 matrix {uu0, uv 0, u,...} <latexit sha1_base64="pcmig45y9xtmgmeuc4o5xyekun0=">aaab/xicbvdlssnafj3uv62v+ni5gsyiixisexrzdooygn1ae8pkommhtizhhouair/ixouibv0pd/6nkzylbt1wl4dz7mxundblvcrx/bzkk6tr6xvlzcrw9s7unr1/0jkjfpg0ccis0qmrjixy0lrumdjjbufxyeg7hn3mfntmhkqjf1ctlaqxgnaauyyukxr2kz9pfvadepy3gnqcx5/27krrudpazeivpaoknhr2l99psi4jv5ghkbuem6ogq0jrzmi04mtjuorhaec6hniuexlks+un8nqofrglwhrxckb+3shqloukds1kjnrqlnq5+j/x1sq6djlku60ix/ohis2gsmaebextqbbie0mqfttccveqcysvcaxiqvawv7xmwheo5zre/ww1flpeuqbh4ascaw9cgtq4aw3qbbg8gmfwct6sj+vferc+5qmlq9g5bh9gff4ascytkq==</latexit> 9x9 matrix X> X N correspondences Longuet-Higgins, "A computer algorithm for reconstructing a scene from two projections. Nature, SVD Essential Matrix
18 Learning to compute weights We learn to compute weights for the 8-point algorithm Nx9 matrix {uu0, uv 0, u,...} 9x9 matrix <latexit sha1_base64="pcmig45y9xtmgmeuc4o5xyekun0=">aaab/xicbvdlssnafj3uv62v+ni5gsyiixisexrzdooygn1ae8pkommhtizhhouair/ixouibv0pd/6nkzylbt1wl4dz7mxundblvcrx/bzkk6tr6xvlzcrw9s7unr1/0jkjfpg0ccis0qmrjixy0lrumdjjbufxyeg7hn3mfntmhkqjf1ctlaqxgnaauyyukxr2kz9pfvadepy3gnqcx5/27krrudpazeivpaoknhr2l99psi4jv5ghkbuem6ogq0jrzmi04mtjuorhaec6hniuexlks+un8nqofrglwhrxckb+3shqloukds1kjnrqlnq5+j/x1sq6djlku60ix/ohis2gsmaebextqbbie0mqfttccveqcysvcaxiqvawv7xmwheo5zre/ww1flpeuqbh4ascaw9cgtq4aw3qbbg8gmfwct6sj+vferc+5qmlq9g5bh9gff4ascytkq==</latexit> X> w X <latexit sha1_base64="dlmfdtqzwoqgkfsgeouhguazgps=">aaacjhicbvdjsgnbeo2jw4xb1koxxhauccomfwuvqs8ei5gfmppq0+ljmvqs9bijw3ymf3/fiwcxphjxw+xjimpiqtev3quiqp4xmyqkzx0yuaxlldw1/hphy3nre6e4u9cqkeky1hheit7ykccmhqquqwskfxocao+rpje8yvtmihbbo/bwjmpibqgfup9ijdxvlv6unuspowpuo+yrqnm0nbtgbegopd9ppr1hrvf3etc5sx+kbrfkmdyk4ckwz6aezlhrfl+dxorvqekjgrkibvuxdbpejcwm6klkkbjhieqttoyhcohwk8mrksxrpgf9iosxsjhhf3ckkbbihhi6mttqzgsz+z/wvti/dxmaxkqsee8h+ypbgchmmdijngdjxhogzknefeib4ghl7wtbm2dpn7wigqembzn2jvwqxs7syimdcaiogq3oqbvcgxqoawzuwsn4bi/gg/fkvbnv09kcmevzb3/c+pwca02j6a==</latexit> sha1_base64="ozm7whtvmxg2ill5gkzzzpldtsg=">aaacjhicbvdjsgnbek2jw4xb1koxxickhghgi4kiohepeuwmzglo6fqktxowelhcepbxvpgruxhwwymxv8werhet6obve1vu1fmtzqrynfcrnze5nt2tny3mzs8slhwxv2oy1olqkol5loo+lpszifyvu5zwe0fx6hn64fdomv3iigrj4uhc9rpadhenygejwbmqvdzf8fktn8tix2vfgdm27q0kxohv1w/s+udsu3hymv5fbg++pfax5njoknbf4h6a0ugwchadajvw8clrx0shnfkeyykbrpoozoqfyortm1vlmmdswx3amddcizxndhtkag0ypo2cwjgxktriv3ekojsyh/qmmttq/tyy8j+tovww10xzlghfiziefgioviwyx1cbcuou7xuaiwbmv0s6wgcijk8fy4l7++s/olzju47tnjmlo2myrx7wyb22wivdoijtqeavcnzceb7g0bqz7q1n62vcmrm+elbhr1hv73kfpbu=</latexit> sha1_base64="hc3b1cm/ub0yvyu95d3xotqjkmm=">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</latexit> SVD Essential Matrix Nx1 weights Deep Net N correspondences
19 Learning to compute weights We learn to compute weights for the 8-point algorithm Nx9 matrix {uu0, uv 0, u,...} 9x9 matrix <latexit sha1_base64="pcmig45y9xtmgmeuc4o5xyekun0=">aaab/xicbvdlssnafj3uv62v+ni5gsyiixisexrzdooygn1ae8pkommhtizhhouair/ixouibv0pd/6nkzylbt1wl4dz7mxundblvcrx/bzkk6tr6xvlzcrw9s7unr1/0jkjfpg0ccis0qmrjixy0lrumdjjbufxyeg7hn3mfntmhkqjf1ctlaqxgnaauyyukxr2kz9pfvadepy3gnqcx5/27krrudpazeivpaoknhr2l99psi4jv5ghkbuem6ogq0jrzmi04mtjuorhaec6hniuexlks+un8nqofrglwhrxckb+3shqloukds1kjnrqlnq5+j/x1sq6djlku60ix/ohis2gsmaebextqbbie0mqfttccveqcysvcaxiqvawv7xmwheo5zre/ww1flpeuqbh4ascaw9cgtq4aw3qbbg8gmfwct6sj+vferc+5qmlq9g5bh9gff4ascytkq==</latexit> X> w X <latexit sha1_base64="dlmfdtqzwoqgkfsgeouhguazgps=">aaacjhicbvdjsgnbeo2jw4xb1koxxhauccomfwuvqs8ei5gfmppq0+ljmvqs9bijw3ymf3/fiwcxphjxw+xjimpiqtev3quiqp4xmyqkzx0yuaxlldw1/hphy3nre6e4u9cqkeky1hheit7ykccmhqquqwskfxocao+rpje8yvtmihbbo/bwjmpibqgfup9ijdxvlv6unuspowpuo+yrqnm0nbtgbegopd9ppr1hrvf3etc5sx+kbrfkmdyk4ckwz6aezlhrfl+dxorvqekjgrkibvuxdbpejcwm6klkkbjhieqttoyhcohwk8mrksxrpgf9iosxsjhhf3ckkbbihhi6mttqzgsz+z/wvti/dxmaxkqsee8h+ypbgchmmdijngdjxhogzknefeib4ghl7wtbm2dpn7wigqembzn2jvwqxs7syimdcaiogq3oqbvcgxqoawzuwsn4bi/gg/fkvbnv09kcmevzb3/c+pwca02j6a==</latexit> sha1_base64="ozm7whtvmxg2ill5gkzzzpldtsg=">aaacjhicbvdjsgnbek2jw4xb1koxxickhghgi4kiohepeuwmzglo6fqktxowelhcepbxvpgruxhwwymxv8werhet6obve1vu1fmtzqrynfcrnze5nt2tny3mzs8slhwxv2oy1olqkol5loo+lpszifyvu5zwe0fx6hn64fdomv3iigrj4uhc9rpadhenygejwbmqvdzf8fktn8tix2vfgdm27q0kxohv1w/s+udsu3hymv5fbg++pfax5njoknbf4h6a0ugwchadajvw8clrx0shnfkeyykbrpoozoqfyortm1vlmmdswx3amddcizxndhtkag0ypo2cwjgxktriv3ekojsyh/qmmttq/tyy8j+tovww10xzlghfiziefgioviwyx1cbcuou7xuaiwbmv0s6wgcijk8fy4l7++s/olzju47tnjmlo2myrx7wyb22wivdoijtqeavcnzceb7g0bqz7q1n62vcmrm+elbhr1hv73kfpbu=</latexit> sha1_base64="hc3b1cm/ub0yvyu95d3xotqjkmm=">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</latexit> Fully differentiable! Deep Net N correspondences SVD Essential Matrix Nx1 weights
20 Learning to compute weights We learn to compute weights for the 8-point algorithm Nx9 matrix {uu0, uv 0, u,...} 9x9 matrix <latexit sha1_base64="pcmig45y9xtmgmeuc4o5xyekun0=">aaab/xicbvdlssnafj3uv62v+ni5gsyiixisexrzdooygn1ae8pkommhtizhhouair/ixouibv0pd/6nkzylbt1wl4dz7mxundblvcrx/bzkk6tr6xvlzcrw9s7unr1/0jkjfpg0ccis0qmrjixy0lrumdjjbufxyeg7hn3mfntmhkqjf1ctlaqxgnaauyyukxr2kz9pfvadepy3gnqcx5/27krrudpazeivpaoknhr2l99psi4jv5ghkbuem6ogq0jrzmi04mtjuorhaec6hniuexlks+un8nqofrglwhrxckb+3shqloukds1kjnrqlnq5+j/x1sq6djlku60ix/ohis2gsmaebextqbbie0mqfttccveqcysvcaxiqvawv7xmwheo5zre/ww1flpeuqbh4ascaw9cgtq4aw3qbbg8gmfwct6sj+vferc+5qmlq9g5bh9gff4ascytkq==</latexit>? X> w X <latexit sha1_base64="dlmfdtqzwoqgkfsgeouhguazgps=">aaacjhicbvdjsgnbeo2jw4xb1koxxhauccomfwuvqs8ei5gfmppq0+ljmvqs9bijw3ymf3/fiwcxphjxw+xjimpiqtev3quiqp4xmyqkzx0yuaxlldw1/hphy3nre6e4u9cqkeky1hheit7ykccmhqquqwskfxocao+rpje8yvtmihbbo/bwjmpibqgfup9ijdxvlv6unuspowpuo+yrqnm0nbtgbegopd9ppr1hrvf3etc5sx+kbrfkmdyk4ckwz6aezlhrfl+dxorvqekjgrkibvuxdbpejcwm6klkkbjhieqttoyhcohwk8mrksxrpgf9iosxsjhhf3ckkbbihhi6mttqzgsz+z/wvti/dxmaxkqsee8h+ypbgchmmdijngdjxhogzknefeib4ghl7wtbm2dpn7wigqembzn2jvwqxs7syimdcaiogq3oqbvcgxqoawzuwsn4bi/gg/fkvbnv09kcmevzb3/c+pwca02j6a==</latexit> sha1_base64="ozm7whtvmxg2ill5gkzzzpldtsg=">aaacjhicbvdjsgnbek2jw4xb1koxxickhghgi4kiohepeuwmzglo6fqktxowelhcepbxvpgruxhwwymxv8werhet6obve1vu1fmtzqrynfcrnze5nt2tny3mzs8slhwxv2oy1olqkol5loo+lpszifyvu5zwe0fx6hn64fdomv3iigrj4uhc9rpadhenygejwbmqvdzf8fktn8tix2vfgdm27q0kxohv1w/s+udsu3hymv5fbg++pfax5njoknbf4h6a0ugwchadajvw8clrx0shnfkeyykbrpoozoqfyortm1vlmmdswx3amddcizxndhtkag0ypo2cwjgxktriv3ekojsyh/qmmttq/tyy8j+tovww10xzlghfiziefgioviwyx1cbcuou7xuaiwbmv0s6wgcijk8fy4l7++s/olzju47tnjmlo2myrx7wyb22wivdoijtqeavcnzceb7g0bqz7q1n62vcmrm+elbhr1hv73kfpbu=</latexit> sha1_base64="hc3b1cm/ub0yvyu95d3xotqjkmm=">aaacjhicbvdjsgnbeo1xjemw9eilmqrfwjdjruheobepecwcmst0dhqsxp6fxpqw5gne8fdy8eccibe/xz4koiywdppqvsqq6nkxo0la9ocxmzs3v7cywtkxv1bx1rmbmxurky5jgucs4jupccjosmqsskzqmsco8bipetfnqv69ivzqklysvzg0atqjqu8xkppqzy/zbqlubggqm/qrqmuy3l7pbkh2pt+p9zuujolv9la53/+rwtmcbdndgnpagypc6ca8ie/fzvir++k2i6wcekrmkbb1x45li0fcusyinqoeirg+rh1s1zbeargnzhhkh+y104z+xpuljryyvzssfajrczxdmw4ojrwu/e+rk+kfnriaxkqsei8g+ypbgchumdimngdjehogzknefeiu4ghl7auptxamt54glqplss3n0s4vz8aommab7ia94ibduaqxoatkaim7mabp4nl4mb6nv+ntvdpjjhu2wj8wpr8azbsnkq==</latexit> SVD Essential Matrix Nx1 weights Deep Net N correspondences
21 Exploiting epipolar geometry We do not have dense depth data. But we have the ground truth camera poses. With epipolar geometry we know that points in image 1 map to lines in image 2. Source: wikipedia (
22 Adding a classification loss Not perfect (point line)! But good enough for a supervision signal. Hartley & Zisserman, Multiple view geometry in computer vision, 2000.
23 Complete formulation We jointly train for outlier rejection and regression to the Essential matrix by minimizing the hybrid loss: Classification (Inliers vs outliers) Regression (which inliers help us retrieve E?)
24 Our network CORRESPONDENCES: N x 4 4-D ( ) P P Shared P 128-D ( ) P P Shared P RESNET BLOCK 1 CONTEXT NORM BATCH NORM + ReLU P P Shared P CONTEXT NORM BATCH NORM + ReLU + + ( ) + RESNET BLOCK ( ) + ( ) RESNET BLOCK D P P Shared P 1-D ReLU ReLU ( ) ReLU tanh tanh ( ) tanh WEIGHTS: N x 1 Input: putative matches (SIFT+NN). Coordinates only: Output: Weights, encoding inlier probability. {u, v, u 0,v 0 } 1appleiappleN <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> Network: MLPs. Global context embedded via Context Normalization.
25 <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> <latexit sha1_base64="pw8vs6kbouutkehspyuviojapaq=">aaacf3icbvdlssnafj34rpuvdelmsjs6kcerqzcfuvxjbfuajpbjdniontycr6ge/oubf8wnc0xc6s6/cdjmoa0hhns4517u3omnjapp29/g0vlk6tp6yao4ubw9s2vu7tdfrdgmdryzmld9jaijewlikhlpj5yg0gek5q8vmr81ilzqolqt44r4iephnkayss11tavshkgo/cc9nbtdvfxhqapvrzeko7lphegy8gdprnxmumbjtuwp4cjxclicoepd88vtxvifjjkyise6jp1il0vcusyixqkesraeoj7pabqhkagvnd41gwwt9gaqc/0icafq74kuhukmq193zkeies8t//m6sgbnxkqjreks4dmiqdeoy5ifbhuueyzzwboeodv/hxiaomjsr1nuitjzjy+s5onl2jzze1qqxevxfmahoalhwafnoaauqr00aaap4bm8gjfjyxgx3o2pweuskc8cgd8wpn8aitgdzw==</latexit> Embedding context Batch CONTEXT NORM Non-parametric normalization of the mean/std of feature maps. Applied over each image pair in the batch separately. Also known as Instance Norm, used in image stylization. Correspondences Image pair Image pair Image pair Perceptron - Mean & Variance Coords: {u, v, u 0,v 0 } 1appleiappleN
26 Results Train on only two sequences: one indoors & one outdoors (10k pairs from each): (i) St. Peter s Square (2.5k images) (ii) Brown (video, 8k images) Test on completely different sequences (1k pairs from each):
27 Results Ours RANSAC Outdoors: great performance. Indoors: slightly better than dense methods.
28 RANSAC (SIFT, 2000 keypoints) OURS (SIFT, 2000 keypoints)
29 Collaborators Kwang Yi (U. Victoria) Eduard Trulls (EPFL) Yuki Ono (Sony) Mathieu Salzmann Vincent Lepetit (EPFL) (U. Bordeaux) Pascal Fua (EPFL) Code and models: github.com/vcg-uvic/learned-correspondence-release
30 Thanks for your attention. Questions?
MoA-Net: Self-supervised Motion Segmentation. Pia Bideau, Rakesh R Menon, Erik Learned-Miller
MoA-Net: Self-supervised Motion Segmentation Pia Bideau, Rakesh R Menon, Erik Learned-Miller University of Massachusetts Amherst College of Information and Computer Science Motion Segmentation P Bideau,
Linear Classification and Logistic Regression. Pascal Fua IC-CVLab
Linear Classification and Logistic Regression Pascal Fua IC-CVLab 1 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
Machine Learning for Data Science (CS4786) Lecture11. Random Projections & Canonical Correlation Analysis
Machine Learning for Data Science (CS4786) Lecture11 5 Random Projections & Canonical Correlation Analysis The Tall, THE FAT AND THE UGLY n X d The Tall, THE FAT AND THE UGLY d X > n X d n = n d d The
Neural Networks (The Machine-Learning Kind) BCS 247 March 2019
Neural Networks (The Machine-Learning Kind) BCS 247 March 2019 Neurons http://biomedicalengineering.yolasite.com/neurons.php Networks https://en.wikipedia.org/wiki/network_theory#/media/file:social_network_analysis_visualization.png
Hard-Margin Support Vector Machines
Hard-Margin Support Vector Machines aaacaxicbzdlssnafiyn9vbjlepk3ay2gicupasvu4iblxuaw2hjmuwn7ddjjmxm1bkcg1/fjqsvt76fo9/gazqfvn8y+pjpozw5vx8zkpvtfxmlhcwl5zxyqrm2vrg5zw3vxmsoezi4ogkr6phieky5crvvjhriqvdom9l2xxftevuwcekj3lktmhghgniauiyutvrwxtvme34a77kbvg73gtygpjsrfati1+xc8c84bvraowbf+uwnipyehcvmkjrdx46vlykhkgykm3ujjdhcyzqkxy0chur6ax5cbg+1m4bbjptjcubuz4kuhvjoql93hkin5hxtav5x6yyqopnsyuneey5ni4keqrxbar5wqaxbik00icyo/iveiyqqvjo1u4fgzj/8f9x67bzmxnurjzmijtlybwfgcdjgfdtajwgcf2dwaj7ac3g1ho1n4814n7wwjgjmf/ys8fenfycuzq==
Gradient Coding using the Stochastic Block Model
Gradient Coding using the Stochastic Block Model Zachary Charles (UW-Madison) Joint work with Dimitris Papailiopoulos (UW-Madison) aaacaxicbvdlssnafj3uv62vqbvbzwarxjsqikaboelgzux7gcaeywtsdp1mwsxeaepd+ctuxcji1r9w5984bbpq1gmxdufcy733bcmjutn2t1fawl5zxsuvvzy2t7z3zn29lkwyguktjywrnqbjwigntuuvi51uebqhjlsdwfxebz8qiwnc79uwjv6mepxgfcoljd88uiox0m1hvlnzwzgowymjn7tjyzertmvpareju5aqkndwzs83thawe64wq1j2httvxo6eopirccxnjekrhqae6wrkuuykl08/gmnjryqwsoqurubu/t2ro1jkyrzozhipvpz3juj/xjdt0ywxu55mina8wxrldkoetukairuekzbubgfb9a0q95fawonqkjoez/7lrdi6trzbcm7pqvwrio4yoarh4aq44bzuwq1ogcba4be8g1fwzjwzl8a78tfrlrnfzd74a+pzb2h+lzm=
deep learning for NLP (5 lectures)
TTIC 31210: Advanced Natural Language Processing Kevin Gimpel Spring 2019 Lecture 6: Finish Transformers; Sequence- to- Sequence Modeling and AJenKon 1 Roadmap intro (1 lecture) deep learning for NLP (5
Machine Learning for Data Science (CS4786) Lecture 11. Spectral Embedding + Clustering
Machine Learning for Data Science (CS4786) Lecture 11 Spectral Embedding + Clustering MOTIVATING EXAMPLE What can you say from this network? MOTIVATING EXAMPLE How about now? THOUGHT EXPERIMENT For each
tum.de/fall2018/ in2357
https://piazza.com/ tum.de/fall2018/ in2357 Prof. Daniel Cremers From to Classification Categories of Learning (Rep.) Learning Unsupervised Learning clustering, density estimation Supervised Learning learning
Label-Noise Robust Generative Adversarial Networks
Label-Noise Robust Generative Adversarial Networks Training data rcgan Noisy labeled Conditioned on clean labels Takuhiro Kaneko1 Yoshitaka Ushiku1 Tatsuya Harada1, 2 1The University of Tokyo 2RIKEN Talk
Logistic Regression. Machine Learning CS5824/ECE5424 Bert Huang Virginia Tech
Logistic Regression Machine Learning CS5824/ECE5424 Bert Huang Virginia Tech Outline Review conditional probability and classification Linear parameterization and logistic function Gradient descent Other
TTIC 31210: Advanced Natural Language Processing. Kevin Gimpel Spring Lecture 9: Inference in Structured Prediction
TTIC 31210: Advanced Natural Language Processing Kevin Gimpel Spring 2019 Lecture 9: Inference in Structured Prediction 1 intro (1 lecture) Roadmap deep learning for NLP (5 lectures) structured prediction
The Overview of Civilian Applications of Airborne SAR Systems
The Overview of Civilian Applications of Airborne SAR Systems Maciej Smolarczyk, Piotr Samczyński Andrzej Gadoś, Maj Mordzonek Research and Development Department of PIT S.A. PART I WHAT DOES SAR MEAN?
Supervised Hierarchical Clustering with Exponential Linkage. Nishant Yadav
Supervised Hierarchical Clustering with Exponential Linage Nishant Yadav Ari Kobren Nicholas Monath Andrew McCallum At train time, learn A :2 X! Y Supervised Clustering aaab8nicbvdlssnafl2pr1pfvzdugvwvrirdfl147kcfuabymq6aydozslmjvbcp8onc0xc+jxu/bsnbrbaemdgcm69zlntaq36hnftmltfwnzq7xd2dnd2z+ohh61juo1zs2qhnldbgmugqt5chyn9gmxkfgnxbyl/udj6ynv/irpwlyjkspokuojv6/zjgmbkr3cwg1zpx9+zwv4lfbouaa6qx/2homnmjfjbjon5xojbrjryktis08nswidbhrwspjzeyqzspp3dordn1iafsunp190zgymomcwgn84hm2cvf/7xeitf1hgzpmgxxwupcjf5eb3u0ouguuxtyrqzw1wl46jjhrtsxvbgr988ippx9r9yx8ua43boo4ynmapnimpv9cae2hccygoeizxehpqexheny/fampdo7hd5zph3bqvc=
TSBB15 Computer Vision
TSBB15 Computer Vision Lecture 10 Recap. of TSBB06, Maximum Likelihood and RANSAC!1 Prelude to Project 2 Image credit: Bundler home page http://www.cs.cornell.edu/~snavely/bundler/ Project 2: Start with
TTIC 31210: Advanced Natural Language Processing. Kevin Gimpel Spring Lecture 8: Structured PredicCon 2
TTIC 31210: Advanced Natural Language Processing Kevin Gimpel Spring 2019 Lecture 8: Structured PredicCon 2 1 Roadmap intro (1 lecture) deep learning for NLP (5 lectures) structured predic+on (4 lectures)
Machine Learning for Data Science (CS4786) Lecture 24. Differential Privacy and Re-useable Holdout
Machine Learning for Data Science (CS4786) Lecture 24 Differential Privacy and Re-useable Holdout Defining Privacy Defining Privacy Dataset + Defining Privacy Dataset + Learning Algorithm Distribution
SSW1.1, HFW Fry #20, Zeno #25 Benchmark: Qtr.1. Fry #65, Zeno #67. like
SSW1.1, HFW Fry #20, Zeno #25 Benchmark: Qtr.1 I SSW1.1, HFW Fry #65, Zeno #67 Benchmark: Qtr.1 like SSW1.2, HFW Fry #47, Zeno #59 Benchmark: Qtr.1 do SSW1.2, HFW Fry #5, Zeno #4 Benchmark: Qtr.1 to SSW1.2,
Towards Stability Analysis of Data Transport Mechanisms: a Fluid Model and an Application
Towards Stability Analysis of Data Transport Mechanisms: a Fluid Model and an Application Gayane Vardoyan *, C. V. Hollot, Don Towsley* * College of Information and Computer Sciences, Department of Electrical
OpenPoland.net API Documentation
OpenPoland.net API Documentation Release 1.0 Michał Gryczka July 11, 2014 Contents 1 REST API tokens: 3 1.1 How to get a token............................................ 3 2 REST API : search for assets
Analysis of Movie Profitability STAT 469 IN CLASS ANALYSIS #2
Analysis of Movie Profitability STAT 469 IN CLASS ANALYSIS #2 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
HOW MASSIVE ARE PROTOPLANETARY/ PLANET HOSTING/PLANET FORMING DISCS?
GREAT BARRIERS IN PLANET FORMATION, PALM COVE 26/07/2019 HOW MASSIVE ARE PROTOPLANETARY/ PLANET HOSTING/PLANET FORMING DISCS? CAN ALL THESE STRUCTURES TELL US SOMETHING ABOUT THE (GAS) DISC MASS? BENEDETTA
ERASMUS + : Trail of extinct and active volcanoes, earthquakes through Europe. SURVEY TO STUDENTS.
ERASMUS + : Trail of extinct and active volcanoes, earthquakes through Europe. SURVEY TO STUDENTS. Strona 1 1. Please give one answer. I am: Students involved in project 69% 18 Student not involved in
Internet of Things Devices
Internet of Things Devices } Internet of Things (IoT) devices } Have access to an abundance of raw data } In home, work, or vehicle IoT: Raw Data & Processing } IoT is gaining ground with the widespread
Helena Boguta, klasa 8W, rok szkolny 2018/2019
Poniższy zbiór zadań został wykonany w ramach projektu Mazowiecki program stypendialny dla uczniów szczególnie uzdolnionych - najlepsza inwestycja w człowieka w roku szkolnym 2018/2019. Składają się na
Bayesian graph convolutional neural networks
Bayesian graph convolutional neural networks Mark Coates Collaborators: Soumyasundar Pal, Yingxue Zhang, Deniz Üstebay McGill University, Huawei Noah s Ark Lab February 13, 2019 Montreal 2 / 36 Introduction
Klaps za karę. Wyniki badania dotyczącego postaw i stosowania kar fizycznych. Joanna Włodarczyk
Klaps za karę Wyniki badania dotyczącego postaw i stosowania kar fizycznych Joanna Włodarczyk joanna.wlodarczyk@fdds.pl Warszawa, 1.12.2017 Fundacja Dajemy Dzieciom Siłę, 2017 Informacje o badaniu Badanie
Revenue Maximization. Sept. 25, 2018
Revenue Maximization Sept. 25, 2018 Goal So Far: Ideal Auctions Dominant-Strategy Incentive Compatible (DSIC) b i = v i is a dominant strategy u i 0 x is welfare-maximizing x and p run in polynomial time
Previously on CSCI 4622
More Naïve Bayes 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
Multimodal Unsupervised Image-to-Image Translation
Multimodal Unsupervised Image-to-Image Translation Xun Huang 1, Ming-Yu Liu 2, Serge Belongie 1, Jan Kautz 2 Cornell University 1 NVIDIA 2 Abstract. Unsupervised image-to-image translation is an important
Network Services for Spatial Data in European Geo-Portals and their Compliance with ISO and OGC Standards
INSPIRE Conference 2010 INSPIRE as a Framework for Cooperation Network Services for Spatial Data in European Geo-Portals and their Compliance with ISO and OGC Standards Elżbieta Bielecka Agnieszka Zwirowicz
MATLAB Neural Network Toolbox przegląd
MATLAB Neural Network Toolbox przegląd WYKŁAD Piotr Ciskowski Neural Network Toolbox: Neural Network Toolbox - zastosowania: przykłady zastosowań sieci neuronowych: The 1988 DARPA Neural Network Study
Rozpoznawanie twarzy metodą PCA Michał Bereta 1. Testowanie statystycznej istotności różnic między jakością klasyfikatorów
Rozpoznawanie twarzy metodą PCA Michał Bereta www.michalbereta.pl 1. Testowanie statystycznej istotności różnic między jakością klasyfikatorów Wiemy, że możemy porównywad klasyfikatory np. za pomocą kroswalidacji.
Komunikacja Człowiek-Komputer
Komunikacja Człowiek-Komputer Przetwarzanie i rozpoznawanie obrazów przegląd Wojciech Jaśkowski Instytut Informatyki Politechnika Poznańska Wersja: 21 listopada 2014 Transformata Hough Detekcja odcinków
Maximum A Posteriori Chris Piech CS109, Stanford University
Maximum A Posteriori Chris Piech CS109, Stanford University Previously in CS109 Game of Estimators Estimators Maximum Likelihood Non spoiler alert: this didn t happen in game of thrones aaab7nicbva9swnbej2lxzf+rs1tfomqm3anghywarvlcoydkjpsbfasjxt7x+6cei78cbslrwz9pxb+gzfjfzr4yodx3gwz84jecoou++0u1ty3nrek26wd3b39g/lhucveqwa8ywiz605adzdc8sykllytae6jqpj2ml6d+e0nro2i1qnoeu5hdkhekbhfk7u7j1lvne/75ypbc+cgq8tlsqvynprlr94gzmneftjjjel6boj+rjukjvm01esntygb0yhvwqpoxi2fzc+dkjordegya1skyvz9pzhryjhjfnjoiolilhsz8t+vm2j47wdcjslyxralwlqsjmnsdziqmjoue0so08lestiiasrqjlsyixjll6+s1kxnc2ve/wwlfpphuyqtoiuqehafdbidbjsbwrie4rxenmr5cd6dj0vrwclnjuepnm8fuskpig==
archivist: Managing Data Analysis Results
archivist: Managing Data Analysis Results https://github.com/pbiecek/archivist Marcin Kosiński 1,2, Przemysław Biecek 2 1 IT Research and Development Grupa Wirtualna Polska 2 Faculty of Mathematics, Informatics
16. kwietnia Znajdowanie sekwencji w bazach danych. Bartek Wilczy«ski. Organizacyjne. Why search for sequences
16. kwietnia 2019 Materiaªy do dzisiejszego wykªadu: http://www.mimuw.edu.pl/ dojer/wobm/alg-heuryst.pdf How much similarity is there? Many important genes are conserved between distantly related species
Rozpoznawanie obiektów z użyciem znaczników
Rozpoznawanie obiektów z użyciem znaczników Sztuczne znaczniki w lokalizacji obiektów (robotów) Aktywne znaczniki LED do lokalizacji w przestrzeni 2D (do 32): Znaczniki z biblioteki AruCo (do 1024) Id
1. Wprowadzenie. Tomasz Kornuta
Pomiary Automatyka Robotyka, ISSN 1427-9126, R. 21, Nr 1/2017, 19 24, DOI: 10.14313/PAR_223/19 Tomasz Kornuta Dwuczęściowy artykuł dotyczy problemu rejestracji obrazów RGB-D. W robotyce problem ten znany
Wykaz linii kolejowych, które są wyposażone w urządzenia systemu ETCS
Wykaz kolejowych, które są wyposażone w urządzenia W tablicy znajdującej się na kolejnych stronach tego załącznika zastosowano następujące oznaczenia: - numer kolejowej według instrukcji Wykaz Id-12 (D-29).
Formularz recenzji magazynu. Journal of Corporate Responsibility and Leadership Review Form
Formularz recenzji magazynu Review Form Identyfikator magazynu/ Journal identification number: Tytuł artykułu/ Paper title: Recenzent/ Reviewer: (imię i nazwisko, stopień naukowy/name and surname, academic
Wykaz linii kolejowych, które są wyposażone w urzadzenia systemu ETCS
Wykaz kolejowych, które są wyposażone w urzadzenia W tablicy znajdującej się na kolejnych stronach tego załącznika zastosowano następujące oznaczenia: - numer kolejowej według instrukcji Wykaz Id-12 (D-29).
Horseshoe Priors for Bayesian Neural Networks
Horseshoe Priors for Bayesian Neural Networks Soumya Ghosh Jiayu Yao Finale Doshi-Velez IBM Research Harvard Harvard MIT-IBM Watson AI lab 1 aaacbxicbzdlssnafiyn9vbrlepsf4nfqagleue3qtgnywr2am0ik+mkhtqzctmtmyru3pgqblwo4tz3cofbogmz0oopax//oyc55w9irpv2nc+rtlc4tlxsxq2srw9sbtnbo20leoljcwsmzddaijdksuttzug3lgrfasodyhyv1zt3rcoq+k1oy+jfamhpsdhsxvlt/rrewjhfj5aeycsyzqr2fwspirdued+uonvnkvgx3akqofdttz/7a4gtihcngvkq5zqx9jikncwmtcr9rjey4teakp5bjikivgx6xqqegmcaqyhn4xpo3z8tgyqusqpadobbqvlabv5x6yu6ppcyyunee45nh4ujg1rapbi4ojjgzvidcetqdov4hctc2grxmsg48yf/hfzj3tv8c1ptxbzxlmeeoaa14iiz0adxoalaaimh
Wprowadzenie do programu RapidMiner, część 5 Michał Bereta
Wprowadzenie do programu RapidMiner, część 5 Michał Bereta www.michalbereta.pl 1. Przekształcenia atrybutów (ang. attribute reduction / transformation, feature extraction). Zamiast wybierad częśd atrybutów
No matter how much you have, it matters how much you need
CSR STRATEGY KANCELARIA FINANSOWA TRITUM GROUP SP. Z O.O. No matter how much you have, it matters how much you need Kancelaria Finansowa Tritum Group Sp. z o.o. was established in 2007 we build trust among
MONAA: a Tool for Timed Pattern Matching with Automata-Based Acceleration
https://github.com/maswag/monaa MONAA: a Tool for Timed Pattern Matching with Automata-Based Acceleration Masaki Waga 1, Ichiro Hasuo 1, and Kohei Suenaga 2 National Institute of Informatics 1 and Kyoto
MaPlan Sp. z O.O. Click here if your download doesn"t start automatically
Mierzeja Wislana, mapa turystyczna 1:50 000: Mikoszewo, Jantar, Stegna, Sztutowo, Katy Rybackie, Przebrno, Krynica Morska, Piaski, Frombork =... = Carte touristique (Polish Edition) MaPlan Sp. z O.O Click
Goodman Kraków Airport Logistics Centre. 62,350 sqm available. Units from 1,750 sqm for immediate lease. space for growth+
Goodman Kraków Airport Logistics Centre 62,350 sqm available. Units from 1,750 sqm for immediate lease. space for growth Goodman Kraków Airport Logistics Centre ul. Komandosów 1, 32-085 Modlniczka Goodman
DODATKOWE ĆWICZENIA EGZAMINACYJNE
I.1. X Have a nice day! Y a) Good idea b) See you soon c) The same to you I.2. X: This is my new computer. Y: Wow! Can I have a look at the Internet? X: a) Thank you b) Go ahead c) Let me try I.3. X: What
A hybrid quantum-classical recommender system. Andrea Skolik
A hybrid quantum-classical recommender system Andrea Skolik A recommender system built on matrix factorization got inspired by nonnegative/binary matrix factorization at Qubits 2018 [1] Q: where can we
Knovel Math: Jakość produktu
Knovel Math: Jakość produktu Knovel jest agregatorem materiałów pełnotekstowych dostępnych w formacie PDF i interaktywnym. Narzędzia interaktywne Knovel nie są stworzone wokół specjalnych algorytmów wymagających
Compressing the information contained in the different indexes is crucial for performance when implementing an IR system
4.2 Compression Compressing the information contained in the different indexes is crucial for performance when implementing an IR system on current hardware it is typically much faster to read compressed
ARNOLD. EDUKACJA KULTURYSTY (POLSKA WERSJA JEZYKOWA) BY DOUGLAS KENT HALL
Read Online and Download Ebook ARNOLD. EDUKACJA KULTURYSTY (POLSKA WERSJA JEZYKOWA) BY DOUGLAS KENT HALL DOWNLOAD EBOOK : ARNOLD. EDUKACJA KULTURYSTY (POLSKA WERSJA Click link bellow and free register
DEFINING REGIONS THAT CONTAIN COMPLEX ASTRONOMICAL STRUCTURES
KATY MCKEOUGH XIAO-LI MENG, VINAY KASHYAP, ANETA SIEMIGINOWSKA, DAVID VAN DYK, SHIHAO YANG, LUIS CAMPOS, DEFINING REGIONS THAT CONTAIN COMPLEX ASTRONOMICAL STRUCTURES INTRODUCTION SCIENTIFIC MOTIVATION
ABOUT NEW EASTERN EUROPE BESTmQUARTERLYmJOURNAL
ABOUT NEW EASTERN EUROPE BESTmQUARTERLYmJOURNAL Formanminsidemlookmatmpoliticsxmculturexmsocietymandm economyminmthemregionmofmcentralmandmeasternm EuropexmtheremismnomothermsourcemlikemNew Eastern EuropeImSincemitsmlaunchminmPw--xmthemmagazinemhasm
Zakopane, plan miasta: Skala ok. 1: = City map (Polish Edition)
Zakopane, plan miasta: Skala ok. 1:15 000 = City map (Polish Edition) Click here if your download doesn"t start automatically Zakopane, plan miasta: Skala ok. 1:15 000 = City map (Polish Edition) Zakopane,
Łukasz Reszka Wiceprezes Zarządu
Łukasz Reszka Wiceprezes Zarządu Time for changes! Vocational activisation young unemployed people aged 15 to 24 Projekt location Ząbkowice Śląskie project produced in cooperation with Poviat Labour Office
Installation of EuroCert software for qualified electronic signature
Installation of EuroCert software for qualified electronic signature for Microsoft Windows systems Warsaw 28.08.2019 Content 1. Downloading and running the software for the e-signature... 3 a) Installer
The impact of the global gravity field models on the orbit determination of LAGEOS satellites
models on the Satelitarne metody wyznaczania pozycji we współczesnej geodezji i nawigacji, Poland 2-4.06.2011 Krzysztof Sośnica, Daniela Thaller, Adrian Jäggi, Rolf Dach and Gerhard Beutler Astronomical
Tychy, plan miasta: Skala 1: (Polish Edition)
Tychy, plan miasta: Skala 1:20 000 (Polish Edition) Poland) Przedsiebiorstwo Geodezyjno-Kartograficzne (Katowice Click here if your download doesn"t start automatically Tychy, plan miasta: Skala 1:20 000
The Lorenz System and Chaos in Nonlinear DEs
The Lorenz System and Chaos in Nonlinear DEs April 30, 2019 Math 333 p. 71 in Chaos: Making a New Science by James Gleick Adding a dimension adds new possible layers of complexity in the phase space of
STATYSTYKA OD PODSTAW Z SYSTEMEM SAS. wersja 9.2 i 9.3. Szkoła Główna Handlowa w Warszawie
STATYSTYKA OD PODSTAW Z SYSTEMEM SAS wersja 9.2 i 9.3 Szkoła Główna Handlowa w Warszawie Spis treści Wprowadzenie... 6 1. Podstawowe informacje o systemie SAS... 9 1.1. Informacje ogólne... 9 1.2. Analityka...
An employer s statement on the posting of a worker to the territory of the Republic of Poland
Państwowa Inspekcja Pracy Annotation Główny Inspektorat Pracy ul. Barska 28/30 02-315 Warszawa Rzeczypospolita Polska Polska An employer s statement on the posting of a worker to the territory of the Republic
What does it mean to learn in deep networks? And, how does one detect adversarial attacks?
What does it mean to learn in deep networks? And, how does one detect adversarial attacks? Ciprian A. Corneanu Univ. Barcelona Meysam Madadi CVC, UAB Sergio Escalera Univ. Barcelona Aleix M. Martinez OSU
Convolution semigroups with linear Jacobi parameters
Convolution semigroups with linear Jacobi parameters Michael Anshelevich; Wojciech Młotkowski Texas A&M University; University of Wrocław February 14, 2011 Jacobi parameters. µ = measure with finite moments,
y = The Chain Rule Show all work. No calculator unless otherwise stated. If asked to Explain your answer, write in complete sentences.
The Chain Rule Show all work. No calculator unless otherwise stated. If asked to Eplain your answer, write in complete sentences. 1. Find the derivative of the functions y 7 (b) (a) ( ) y t 1 + t 1 (c)
harmonic functions and the chromatic polynomial
harmonic functions and the chromatic polynomial R. Kenyon (Brown) based on joint work with A. Abrams, W. Lam The chromatic polynomial with n colors. G(n) of a graph G is the number of proper colorings
DM-ML, DM-FL. Auxiliary Equipment and Accessories. Damper Drives. Dimensions. Descritpion
DM-ML, DM-FL Descritpion DM-ML and DM-FL actuators are designed for driving round dampers and square multi-blade dampers. Example identification Product code: DM-FL-5-2 voltage Dimensions DM-ML-6 DM-ML-8
KONSPEKT DO LEKCJI MATEMATYKI W KLASIE 3 POLO/ A LAYER FOR CLASS 3 POLO MATHEMATICS
KONSPEKT DO LEKCJI MATEMATYKI W KLASIE 3 POLO/ A LAYER FOR CLASS 3 POLO MATHEMATICS Temat: Funkcja logarytmiczna (i wykładnicza)/ Logarithmic (and exponential) function Typ lekcji: Lekcja ćwiczeniowa/training
Wojewodztwo Koszalinskie: Obiekty i walory krajoznawcze (Inwentaryzacja krajoznawcza Polski) (Polish Edition)
Wojewodztwo Koszalinskie: Obiekty i walory krajoznawcze (Inwentaryzacja krajoznawcza Polski) (Polish Edition) Robert Respondowski Click here if your download doesn"t start automatically Wojewodztwo Koszalinskie:
Poniżej moje uwagi po zapoznaniu się z prezentowanymi zasadami:
Witam wszystkich nawigatorów. Ostatnio zostały opublikowane nowe zasady CEC (opracowane przez Węgrów) dla zawodników i organizatorów CEC 2011, które obowiązują od tego sezonu. Choć w większości pokrywają
Katowice, plan miasta: Skala 1: = City map = Stadtplan (Polish Edition)
Katowice, plan miasta: Skala 1:20 000 = City map = Stadtplan (Polish Edition) Polskie Przedsiebiorstwo Wydawnictw Kartograficznych im. Eugeniusza Romera Click here if your download doesn"t start automatically
Let's talk about art 07. Porozmawiajmy o sztuce
Let's talk about art 07. Porozmawiajmy o sztuce 1 Ray Magazine 2/2019 2 07. EN LET S TALK ABOUT ART WARAT RODPECHPRAI Thailand - 1st place 1. What encouraged you to take part in the competition? The competition
CEE 111/211 Agenda Feb 17
CEE 111/211 Agenda Feb 17 Tuesday: SW for project work: Jetstream, MSP, Revit, Riuska, POP, SV On R: drive; takes time to install Acoustics today: \\cife server\files\classes\cee111\presentations Thursday:
Wrocław University of Technology. Uczenie głębokie. Maciej Zięba
Wrocław University of Technology Uczenie głębokie Maciej Zięba UCZENIE GŁĘBOKIE (ang. deep learning) = klasa metod uczenia maszynowego, gdzie model ma strukturę hierarchiczną złożoną z wielu nieliniowych
POLITECHNIKA ŚLĄSKA INSTYTUT AUTOMATYKI ZAKŁAD SYSTEMÓW POMIAROWYCH
POLITECHNIKA ŚLĄSKA INSTYTUT AUTOMATYKI ZAKŁAD SYSTEMÓW POMIAROWYCH Gliwice, wrzesień 2005 Pomiar napięcia przemiennego Cel ćwiczenia Celem ćwiczenia jest zbadanie dokładności woltomierza cyfrowego dla
www.irs.gov/form990. If "Yes," complete Schedule A Schedule B, Schedule of Contributors If "Yes," complete Schedule C, Part I If "Yes," complete Schedule C, Part II If "Yes," complete Schedule C, Part
Nonlinear data assimilation for ocean applications
Nonlinear data assimilation for ocean applications Peter Jan van Leeuwen, Manuel Pulido, Jacob Skauvold, Javier Amezcua, Polly Smith, Met Ades, Mengbin Zhu Comparing observations and models Ensure they
Machine Learning for Data Science (CS4786) Lecture 8. Kernel PCA & Isomap + TSNE
Machie Learig for Data Sciece (CS4786) Lecture 8 Kerel PCA & Isomap + TSNE LINEAR PROJECTIONS X d W = Y K d K Works whe data lies i a low dimesioal liear sub-space KERNEL TRICK ( ) ( ) We have have ice
Lips code 381. *ASP = Lodz National School of Art. 40 szt. ROZMIARY: 2/S, 3/M, 4/L. nero
SPRING/SUMMER 2017 ASP Collection ASP Collection to unikalne wzory przygotowane przez studentów Akademii Sztuk Pięknych w Łodzi. Kolekcja przenosi awangardę do codziennych stylizacji. Dzięki temu każda
Goodman Poznań Airport Logistics Centre 16,734 sqm warehouse space available as from Q3 2016. Best placed for business+
Goodman Poznań Airport 16,734 sqm warehouse space available as from Q3 2016. Best placed for business Goodman Poznań Airport ul. Batorowska 35, 62-081 Wysogotowo, Poznań Najlepsze miejsce dla biznesu Goodman
Configuring and Testing Your Network
Configuring and Testing Your Network Network Fundamentals Chapter 11 Version 4.0 1 Konfigurowanie i testowanie Twojej sieci Podstawy sieci Rozdział 11 Version 4.0 2 Objectives Define the role of the Internetwork
New Roads to Cryptopia. Amit Sahai. An NSF Frontier Center
New Roads to Cryptopia Amit Sahai An NSF Frontier Center OPACity Panel, May 19, 2019 New Roads to Cryptopia What about all this space? Cryptography = Hardness* PKE RSA MPC DDH ZK Signatures Factoring IBE
2. Zarys metody SIFT (Scale Invariant Feature Transform)
PIOTR PAWLIK *, SŁAWOMIR MIKRUT ** WYSZUKIWANIE PUNKTÓW CHARAKTERYSTYCZNYCH NA POTRZEBY ŁĄCZENIA ZDJĘĆ LOTNICZYCH *** 1.Wstęp Artykuł dotyczy problemu dopasowania fotogrametrycznych zdjęć lotniczych. Istotą
Detekcja punktów zainteresowania
Informatyka, S2 sem. Letni, 2013/2014, wykład#8 Detekcja punktów zainteresowania dr inż. Paweł Forczmański Katedra Systemów Multimedialnych, Wydział Informatyki ZUT 1 / 61 Proces przetwarzania obrazów
Multimodal Unsupervised Image-to-Image Translation
Multimodal Unsupervised Image-to-Image Translation Xun Huang 1, Ming-Yu Liu 2, Serge Belongie 1, Jan Kautz 2 Cornell University 1 NVIDIA 2 Abstract. Unsupervised image-to-image translation is an important
Primal Formulation. Find u h V h such that. A h (u h, v h )= fv h dx v h V h, where. u h v h dx. A h (u h, v h ) = DGFEM Primal Formulation.
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
Metody klasyfikacji danych zaszumionych. Stanisław Kaźmierczak
Metody klasyfikacji danych zaszumionych Stanisław Kaźmierczak Agenda Szum Źródła szumu Zaszumianie i odszumianie Nauka i testowanie Architektura sieci Wyniki Wnioski oraz kierunki dalszych badań 2 3 Definicja
EXAMPLES OF CABRI GEOMETRE II APPLICATION IN GEOMETRIC SCIENTIFIC RESEARCH
Anna BŁACH Centre of Geometry and Engineering Graphics Silesian University of Technology in Gliwice EXAMPLES OF CABRI GEOMETRE II APPLICATION IN GEOMETRIC SCIENTIFIC RESEARCH Introduction Computer techniques
Prices and Volumes on the Stock Market
Prices and Volumes on the Stock Market Krzysztof Karpio Piotr Łukasiewicz Arkadiusz Orłowski Warszawa, 25-27 listopada 2010 1 Data selection Warsaw Stock Exchange WIG index assets most important for investors
Analiza Sieci Społecznych Pajek
Analiza Sieci Społecznych Pajek Dominik Batorski Instytut Socjologii UW 25 marca 2005 1 Wprowadzenie Regularności we wzorach relacji często są nazywane strukturą. Analiza sieci społecznych jest zbiorem
Anette Breu DATA HARMONISATION STEP BY STEP
Anette Breu DATA HARMONISATION STEP BY STEP Data harmonisation theory Ins tytucj a class overview PodmiotEwidencyjnyLubWladajacy «invariant» {self.administrativelevel < self.lowerlevelunit.administrativelevel}
INSPECTION METHODS FOR QUALITY CONTROL OF FIBRE METAL LAMINATES IN AEROSPACE COMPONENTS
Kompozyty 11: 2 (2011) 130-135 Krzysztof Dragan 1 * Jarosław Bieniaś 2, Michał Sałaciński 1, Piotr Synaszko 1 1 Air Force Institute of Technology, Non Destructive Testing Lab., ul. ks. Bolesława 6, 01-494
03 April 2015 03 kwietnia 2015 r. Zmiana satelity dostarczającego sygnał Animal Planet HD. Change of Delivery Satellite for Animal Planet HD
03 April 2015 03 kwietnia 2015 r. Dear Sirs, Change of Delivery Satellite for Animal Planet HD We refer to the Affiliation Agreement with a Commencement Date of 01/11/2009 between ( DCEL / we ) and you,
Goodman Kraków Airport Logistics Centre. Units from 1,750 sqm for immediate lease. space for growth+
Goodman Kraków Airport Logistics Centre Units from 1,750 sqm for immediate lease space for growth Goodman Kraków Airport Logistics Centre ul. Komandosów 1, 32-085 Modlniczka Goodman Kraków Airport Logistics
Wojewodztwo Koszalinskie: Obiekty i walory krajoznawcze (Inwentaryzacja krajoznawcza Polski) (Polish Edition)
Wojewodztwo Koszalinskie: Obiekty i walory krajoznawcze (Inwentaryzacja krajoznawcza Polski) (Polish Edition) Robert Respondowski Click here if your download doesn"t start automatically Wojewodztwo Koszalinskie:
Emilka szuka swojej gwiazdy / Emily Climbs (Emily, #2)
Emilka szuka swojej gwiazdy / Emily Climbs (Emily, #2) Click here if your download doesn"t start automatically Emilka szuka swojej gwiazdy / Emily Climbs (Emily, #2) Emilka szuka swojej gwiazdy / Emily
STATISTICAL METHODS IN BIOLOGY
STATISTICAL METHODS IN BIOLOGY 1. Introduction 2. Populations and samples 3. Hypotheses testing and parameter estimation 4. Experimental design for biological data 5. Most widely used statistical tests
Latent Dirichlet Allocation Models and their Evaluation IT for Practice 2016
Latent Dirichlet Allocation Models and their Evaluation IT for Practice 2016 Paweł Lula Cracow University of Economics, Poland pawel.lula@uek.krakow.pl Latent Dirichlet Allocation (LDA) Documents Latent
EXPERTS IN ROOFING. Join our talented team and start your career today! Search Tecta America's Careers
Home EXPERTS IN ROOFING Join our talented team and start your career today! Search Tecta America's Careers NATIONWIDE COVERAGE With more than 70 locations and 3,000 roofing professionals nationwide, Tecta