| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a68790a12228f5b99e176e8aacff640a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04N7-0147
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04N7-0137
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0454
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04N7-0127
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04N7-014
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-251 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-246
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H04N7-01 |
| filingDate |
2018-10-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-09-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f7efc1fe5d654ad1008c25e304ca2ec0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4e7b62092a6547f52c587363da9f7d12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_19fdc3731b4d15cbb548c7a9a99b1d07 |
| publicationDate |
2020-09-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-10776688-B2 |
| titleOfInvention |
Multi-frame video interpolation using optical flow |
| abstract |
Video interpolation is used to predict one or more intermediate frames at timesteps defined between two consecutive frames. A first neural network model approximates optical flow data defining motion between the two consecutive frames. A second neural network model refines the optical flow data and predicts visibility maps for each timestep. The two consecutive frames are warped according to the refined optical flow data for each timestep to produce pairs of warped frames for each timestep. The second neural network model then fuses the pair of warped frames based on the visibility maps to produce the intermediate frame for each timestep. Artifacts caused by motion boundaries and occlusions are reduced in the predicted intermediate frames. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022075688-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2020294217-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11599979-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112995715-A |
| priorityDate |
2017-11-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |