LEADER 01541nam0 2200337 i 450 001 SUN0064298 005 20140625025023.238 010 $a05-215-8160-5 100 $a20080507d1997 |0engc50 ba 101 $aeng 102 $aGB 105 $a||||Z 1|||| 200 1 $aRestoration ecology and sustainable development$fedited by Krystyna M. Urbanska, Nigel R. Webb, and Peter J. Edwards 205 $aCambridge : Cambridge university$b1997 210 $aXV$d397 p. : ill ; 24 cm 215 $aPapers from a conference sponsored by the Swiss Federal Institute of Technology Zurich, and others. 620 $dCambridge$3SUNL000024 676 $a333.7153$cRisorse naturali ed energia. Reintegrazione. ricupero, ripristino$v22 702 1$aUrbanska$b, Krystyna M.$3SUNV051125 702 1$aEdwards$b, Peter J.$3SUNV051126 712 02$aWebb, Nigel$3SUNV051123 712 $aCambridge university$3SUNV000097$4650 790 1$aWebb, N. R.$zWebb, Nigel$3SUNV051124 790 1$aUrbanska, K. M.$zUrbanska, Krystyna M.$3SUNV101928 790 1$aEdwards, Peter John$zEdwards, Peter J.$3SUNV101929 790 1$aEdwards, P. J.$zEdwards, Peter J.$3SUNV101930 801 $aIT$bSOL$c20200615$gRICA 912 $aSUN0064298 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17CONS Ed26 $e17FSA1038 20080507 $sBuono 996 $aRestoration ecology and sustainable development$91416305 997 $aUNICAMPANIA LEADER 06705nam 22006375 450 001 996464412203316 005 20220115055825.0 010 $a3-030-69535-2 024 7 $a10.1007/978-3-030-69535-4 035 $a(CKB)4100000011781456 035 $a(MiAaPQ)EBC6501091 035 $a(DE-He213)978-3-030-69535-4 035 $a(PPN)253858836 035 $a(EXLCZ)994100000011781456 100 $a20210224d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Vision ? ACCV 2020$b[electronic resource] $e15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 ? December 4, 2020, Revised Selected Papers, Part III /$fedited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XVIII, 757 p. 245 illus., 229 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12624 311 $a3-030-69534-4 327 $aRecognition and Detection -- End-to-end Model-based Gait Recognition -- Horizontal Flipping Assisted Disentangled Feature Learning for Semi-Supervised Person Re-Identification -- MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings -- Backbone Based Feature Enhancement for Object Detection -- Long-Term Cloth-Changing Person Re-identification -- Any-Shot Object Detection -- Background Learnable Cascade for Zero-Shot Object Detection -- Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation -- COG: COnsistent data auGmentation for object perception -- Synthesizing the Unseen for Zero-shot Object Detection -- Fully Supervised and Guided Distillation for One-Stage Detectors -- Visualizing Color-wise Saliency of Black-Box Image Classification Models -- ERIC: Extracting Relations Inferred from Convolutions -- D2D: Keypoint Extraction with Describe to Detect Approach -- Accurate Arbitrary-Shaped Scene Text Detection via Iterative Polynomial Parameter Regression -- Adaptive Spotting: Deep Reinforcement Object Search in 3D Point Clouds -- Efficient Large-Scale Semantic Visual Localization in 2D Maps -- Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild -- Scale-Aware Polar Representation for Arbitrarily-Shaped Text Detection -- Branch Interaction Network for Person Re-identification -- BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images -- Jointly Discriminating and Frequent Visual Representation Mining -- Discrete Spatial Importance-Based Deep Weighted Hashing -- Low-level Sensor Fusion Network for 3D Vehicle Detection using Radar Range-Azimuth Heatmap and Monocular Image -- MLIFeat: Multi-level information fusion based deep local features -- CLASS: Cross-Level Attention and Supervision for Salient Objects Detection -- Cascaded Transposed Long-range Convolutions for Monocular Depth Estimation -- Optimization, Statistical Methods, and Learning -- Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks -- Large-Scale Cross-Domain Few-Shot Learning -- Channel Pruning for Accelerating Convolutional Neural Networks via Wasserstein Metric -- Progressive Batching for Efficient Non-linear Least Squares -- Fast and Differentiable Message Passing on Pairwise Markov Random Fields -- A Calibration Method for the Generalized Imaging Model with Uncertain Calibration Target Coordinates -- Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network -- Towards Fast and Robust Adversarial Training for Image Classification -- Few-Shot Zero-Shot Learning: Knowledge Transfer with Less Supervision -- Lossless Image Compression Using a Multi-Scale Progressive Statistical Model -- Spatial Class Distribution Shift in Unsupervised Domain Adaptation: Local Alignment Comes to Rescue -- Robot Vision -- Point Proposal based Instance Segmentation with Rectangular Masks for Robot Picking Task -- Multi-task Learning with Future States for Vision-based Autonomous Driving -- MTNAS: Search Multi-Task Networks for Autonomous Driving -- Compact and Fast Underwater Segmentation Network for Autonomous Underwater Vehicles -- L2R GAN: LiDAR-to-Radar Translation -- V2A - Vision to Action: Learning robotic arm actions based on vision and language -- To Filter Prune, or to Layer Prune, That Is The Question. 330 $aThe six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12624 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aPattern recognition systems 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aAutomated Pattern Recognition 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aPattern recognition systems. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aAutomated Pattern Recognition. 676 $a006.37 702 $aIshikawa$b Hiroshi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Cheng-Lin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPajdla$b Tomas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShi$b Jianbo$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996464412203316 996 $aComputer vision-ACCV 2020$91890247 997 $aUNISA LEADER 01870nas 2200517- 450 001 996336328803316 005 20231213213019.0 035 $a(DE-599)ZDB3017186-6 035 $a(CKB)2550000000072019 035 $a(CONSER)sn-93038483- 035 $a(EXLCZ)992550000000072019 100 $a19900518a19909999 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInside EPA's clean air report 210 1$aWashington, DC :$cInside Washington Publishers,$d1990- 215 $a1 online resource 300 $aTitle from caption. 300 $a"An exclusive biweekly report on the Clean Air Act and U.S. air policy." 311 08$aPrint version: Inside EPA's clean air report. 2164-7836 (DLC)sn 93038483 (OCoLC)321480512 517 1 $aClean air report 606 $aAir$xPollution$xGovernment policy$zUnited States$vPeriodicals 606 $aAir$xPollution$xLaw and legislation$zUnited States$vPeriodicals 606 $aAir Pollution$xprevention & control 606 $aAir$xPollution$xGovernment policy$2fast$3(OCoLC)fst00802107 606 $aAir$xPollution$xLaw and legislation$2fast$3(OCoLC)fst00802116 607 $aUnited States 607 $aUnited States$2fast 608 $aLegislation. 608 $aPeriodical. 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aAir$xPollution$xGovernment policy 615 0$aAir$xPollution$xLaw and legislation 615 2$aAir Pollution$xprevention & control. 615 7$aAir$xPollution$xGovernment policy. 615 7$aAir$xPollution$xLaw and legislation. 676 $a363.7 712 02$aInside Washington Publishers. 906 $aJOURNAL 912 $a996336328803316 920 $aexl_impl conversion 996 $aInside EPA's clean air report$92335832 997 $aUNISA