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-index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
-0,YFCC100M,yfcc_100m,0.0,0.0,,,a6e695ddd07aad719001c0fc1129328452385949,main,,The New Data and New Challenges in Multimedia Research,2015
-1,YFCC100M,yfcc_100m,45.5039761,-73.5749687,McGill University,edu,7d0ff6d0621b3846e8543bc162fd0215d8adfaf0,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Iscen_Efficient_Large-Scale_Similarity_CVPR_2016_paper.pdf,Efficient Large-Scale Similarity Search Using Matrix Factorization,2016
-2,YFCC100M,yfcc_100m,42.3583961,-71.09567788,MIT,edu,8c192cd39f90eb8ff2969f8916ef8967607c5298,citation,http://pdfs.semanticscholar.org/9677/d2f6a994f598c1d631038d49401c5f707ee0.pdf,"See, Hear, and Read: Deep Aligned Representations",2017
-3,YFCC100M,yfcc_100m,47.5612651,7.5752961,University of Basel,edu,b7c8452ac9791563d9a739bd079b05e518b20aea,citation,http://pdfs.semanticscholar.org/b7c8/452ac9791563d9a739bd079b05e518b20aea.pdf,Web Video in Numbers - An Analysis of Web-Video Metadata,2017
-4,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,7060f6062ba1cbe9502eeaaf13779aa1664224bb,citation,http://cs.stanford.edu/groups/vision/pdf/hata2017cscw.pdf,A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality,2017
-5,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,258dda85eadcd2081d1e0131826aceac7f1e2415,citation,http://pdfs.semanticscholar.org/e62d/40940a2711c7adca2857110272fb34d70576.pdf,Supervision Beyond Manual Annotations for Learning Visual Representations,2016
-6,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016
-7,YFCC100M,yfcc_100m,47.6423318,-122.1369302,Microsoft,company,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016
-8,YFCC100M,yfcc_100m,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015
-9,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,18fe63c013983bea53be7d559ef36a1f385ca6ea,citation,http://pdfs.semanticscholar.org/18fe/63c013983bea53be7d559ef36a1f385ca6ea.pdf,Supervision Beyond Human Annotations for Learning Visual Representations,2015
-10,YFCC100M,yfcc_100m,33.776033,-84.39884086,Georgia Institute of Technology,edu,629b1bdf4d96bb41f7d3fce5c7d5617515303b71,citation,http://pdfs.semanticscholar.org/629b/1bdf4d96bb41f7d3fce5c7d5617515303b71.pdf,Diving Deeper into IM2GPS,2016
-11,YFCC100M,yfcc_100m,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016
-12,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,d0ac9913a3b1784f94446db2f1fb4cf3afda151f,citation,http://pdfs.semanticscholar.org/d0ac/9913a3b1784f94446db2f1fb4cf3afda151f.pdf,Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning,2016
-13,YFCC100M,yfcc_100m,40.72925325,-73.99625394,New York University,edu,18078e72bddefffc24a6e882790aca8531773bed,citation,https://arxiv.org/pdf/1601.02306v1.pdf,Sublinear scaling of country attractiveness observed from Flickr dataset,2015
-14,YFCC100M,yfcc_100m,42.3583961,-71.09567788,MIT,edu,9677d2f6a994f598c1d631038d49401c5f707ee0,citation,https://arxiv.org/pdf/1706.00932.pdf,"See, Hear, and Read: Deep Aligned Representations",2017
-15,YFCC100M,yfcc_100m,42.3583961,-71.09567788,MIT,edu,1b6f3139b1e59b90ab1aaf978359229b75985b49,citation,http://pdfs.semanticscholar.org/847e/39b52a63a55fb94fff7ade1f90a7c67e508b.pdf,Learning with a Wasserstein Loss,2015
-16,YFCC100M,yfcc_100m,33.5934539,130.3557837,Information Technologies Institute,edu,ea985e35b36f05156f82ac2025ad3fe8037be0cd,citation,http://pdfs.semanticscholar.org/ea98/5e35b36f05156f82ac2025ad3fe8037be0cd.pdf,CERTH/CEA LIST at MediaEval Placing Task 2015,2015
-17,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,518f3cb2c9f2481cdce7741c5a821c26378b75e9,citation,http://pdfs.semanticscholar.org/518f/3cb2c9f2481cdce7741c5a821c26378b75e9.pdf,The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition,2016
-18,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,982ede05154c1afdcf6fc623ba45186a34f4b9f2,citation,https://doi.org/10.1109/TMM.2017.2659221,The Many Shades of Negativity,2017
-19,YFCC100M,yfcc_100m,-33.8809651,151.20107299,University of Technology Sydney,edu,982ede05154c1afdcf6fc623ba45186a34f4b9f2,citation,https://doi.org/10.1109/TMM.2017.2659221,The Many Shades of Negativity,2017
-20,YFCC100M,yfcc_100m,46.0658836,11.1159894,University of Trento,edu,982ede05154c1afdcf6fc623ba45186a34f4b9f2,citation,https://doi.org/10.1109/TMM.2017.2659221,The Many Shades of Negativity,2017
-21,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,5996001b797ab2a0f55d5355cb168f25bfe56bbd,citation,http://doi.acm.org/10.1145/2671188.2749398,Content-Based Video Search over 1 Million Videos with 1 Core in 1 Second,2015
-22,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,65c978a97f54cf255f01c6846d6c51b37c61f836,citation,http://pdfs.semanticscholar.org/65c9/78a97f54cf255f01c6846d6c51b37c61f836.pdf,A Glimpse Far into the Future: Understanding Long-term Crowd Worker Accuracy,2016
-23,YFCC100M,yfcc_100m,47.6543238,-122.30800894,University of Washington,edu,301486e8dad7a41a1a99fd6fba28ce153fe1e56e,citation,http://pdfs.semanticscholar.org/3014/86e8dad7a41a1a99fd6fba28ce153fe1e56e.pdf,Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects,2016
-24,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,01a903739564f575b81c87f7a9e2cb7b609f7ada,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Johnson_Image_Retrieval_Using_2015_CVPR_paper.pdf,Image retrieval using scene graphs,2015
-25,YFCC100M,yfcc_100m,31.30104395,121.50045497,Fudan University,edu,c5e37630d0672e4d44f7dee83ac2c1528be41c2e,citation,http://dl.acm.org/citation.cfm?id=3078973,Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction,2017
-26,YFCC100M,yfcc_100m,37.3936717,-122.0807262,Facebook,company,05818eddd8a35fed7f3041d591ef966f8e79bd9a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_003_ext.pdf,Web scale photo hash clustering on a single machine,2015
-27,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,22954dd92a795d7f381465d1b353bcc41901430d,citation,http://pdfs.semanticscholar.org/3b04/f759e9b3c21defe2227374a008bec67751e3.pdf,Learning Visual Storylines with Skipping Recurrent Neural Networks,2016
-28,YFCC100M,yfcc_100m,47.6423318,-122.1369302,Microsoft,company,9bbc952adb3e3c6091d45d800e806d3373a52bac,citation,https://pdfs.semanticscholar.org/9bbc/952adb3e3c6091d45d800e806d3373a52bac.pdf,Learning Visual Classifiers using Human-centric Annotations,2015
-29,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,2c761495cf3dd320e229586f80f868be12360d4e,citation,http://arxiv.org/abs/1707.02968,Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,2017
-30,YFCC100M,yfcc_100m,32.87935255,-117.23110049,"University of California, San Diego",edu,a9be20954e9177d8b2bc39747acdea4f5496f394,citation,http://acsweb.ucsd.edu/~yuw176/report/cvpr_2016.pdf,Event-Specific Image Importance,2016
-31,YFCC100M,yfcc_100m,52.3553655,4.9501644,University of Amsterdam,edu,256f09fe3163564958381d7f3727b5c27c19144c,citation,http://doi.acm.org/10.1145/2733373.2806335,Image2Emoji: Zero-shot Emoji Prediction for Visual Media,2015
-32,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,891433740bf6d318782c468638722aebf8bef2f5,citation,http://pdfs.semanticscholar.org/8914/33740bf6d318782c468638722aebf8bef2f5.pdf,Multi-Frame Video Super-Resolution Using Convolutional Neural Networks,2016
-33,YFCC100M,yfcc_100m,47.6543238,-122.30800894,University of Washington,edu,85304f24f5a1800e66de20ad05e20c8c032b7d03,citation,http://pdfs.semanticscholar.org/8530/4f24f5a1800e66de20ad05e20c8c032b7d03.pdf,Understanding and Discovering Deliberate Self-harm Content in Social Media,2017
-34,YFCC100M,yfcc_100m,22.2081469,114.25964115,University of Hong Kong,edu,35ec869dd0637c933d35ab823202c13b9b5d9aad,citation,http://pdfs.semanticscholar.org/4498/06bcb0987db60a0f8647380f9c335078fb46.pdf,Effective Community Search for Large Attributed Graphs,2016
-35,YFCC100M,yfcc_100m,40.4319722,-86.92389368,Purdue University,edu,7c5dde400571fd357d1093e1829a8bd7917d8fcd,citation,https://arxiv.org/pdf/1704.05982.pdf,Retrospective Higher-Order Markov Processes for User Trails,2017
-36,YFCC100M,yfcc_100m,37.43131385,-122.16936535,Stanford University,edu,9ded64e83d3ba51513ea00de27c0c770a02b0cf4,citation,http://pdfs.semanticscholar.org/9ded/64e83d3ba51513ea00de27c0c770a02b0cf4.pdf,Image Classification using Transfer Learning from Siamese Networks based on Text Metadata Similarity,2016
-37,YFCC100M,yfcc_100m,1.2962018,103.77689944,National University of Singapore,edu,7d621ec871a03a01f5aa65253e9ae6c8aadaf798,citation,http://pdfs.semanticscholar.org/fa2a/0fd5c5d5d3f14bf3875d531372ba6957748d.pdf,Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades,2015
-38,YFCC100M,yfcc_100m,37.4585796,-122.17560525,SRI International,edu,33737f966cca541d5dbfb72906da2794c692b65b,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.238,Spotting Audio-Visual Inconsistencies (SAVI) in Manipulated Video,2017
-39,YFCC100M,yfcc_100m,52.3553655,4.9501644,University of Amsterdam,edu,33737f966cca541d5dbfb72906da2794c692b65b,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.238,Spotting Audio-Visual Inconsistencies (SAVI) in Manipulated Video,2017
-40,YFCC100M,yfcc_100m,42.3583961,-71.09567788,MIT,edu,988aa2583c63ada43ca260dd8b5a4a543725a483,citation,http://pdfs.semanticscholar.org/988a/a2583c63ada43ca260dd8b5a4a543725a483.pdf,Choosing the Right Home Location Definition Method for the Given Dataset,2015
-41,YFCC100M,yfcc_100m,32.9820799,-96.7566278,University of Texas at Dallas,edu,ac9516a589901f1421e8ce905dd8bc5b689317ca,citation,http://pdfs.semanticscholar.org/ac95/16a589901f1421e8ce905dd8bc5b689317ca.pdf,A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data,2016
-42,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,d3008b4122e50a28f6cc1fa98ac6af28b42271ea,citation,http://dl.acm.org/citation.cfm?id=2806218,Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision,2015
-43,YFCC100M,yfcc_100m,-33.8809651,151.20107299,University of Technology Sydney,edu,d3008b4122e50a28f6cc1fa98ac6af28b42271ea,citation,http://dl.acm.org/citation.cfm?id=2806218,Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision,2015
-44,YFCC100M,yfcc_100m,38.0353682,-78.5035322,University of Virginia,edu,17e7a53456539dac2c9cf8631174c6388f64e24b,citation,https://arxiv.org/pdf/1612.01635.pdf,Learning to Detect Multiple Photographic Defects,2018
-45,YFCC100M,yfcc_100m,22.2081469,114.25964115,University of Hong Kong,edu,5d1ffb7ba3c53ecc5a90d40380ae235043c16344,citation,http://pdfs.semanticscholar.org/5d1f/fb7ba3c53ecc5a90d40380ae235043c16344.pdf,On Label-Aware Community Search,2016
-46,YFCC100M,yfcc_100m,35.9020448,139.93622009,University of Tokyo,edu,81f63e7344cc242416e37d791f7eb83ec2c07681,citation,https://arxiv.org/pdf/1804.06057.pdf,Multimodal Co-Training for Selecting Good Examples from Webly Labeled Video,2018
-47,YFCC100M,yfcc_100m,-37.8087465,144.9638875,RMIT University,edu,3ad6bd5c34b0866019b54f5976d644326069cb3d,citation,http://pdfs.semanticscholar.org/3ad6/bd5c34b0866019b54f5976d644326069cb3d.pdf,Towards Next Generation Touring: Personalized Group Tours,2016
-48,YFCC100M,yfcc_100m,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,02b852e698dfe85df39c24e7dd39dedf484893dd,citation,http://pdfs.semanticscholar.org/02b8/52e698dfe85df39c24e7dd39dedf484893dd.pdf,Collaborative Learning for Weakly Supervised Object Detection,2018
-49,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,187480101af3fb195993da1e2c17d917df24eb23,citation,http://arxiv.org/pdf/1505.05192v2.pdf,Unsupervised Visual Representation Learning by Context Prediction,2015
-50,YFCC100M,yfcc_100m,37.8687126,-122.25586815,"University of California, Berkeley",edu,187480101af3fb195993da1e2c17d917df24eb23,citation,http://arxiv.org/pdf/1505.05192v2.pdf,Unsupervised Visual Representation Learning by Context Prediction,2015
-51,YFCC100M,yfcc_100m,31.846918,117.29053367,Hefei University of Technology,edu,beeadf57a976f23f4fd6fa8a330eac6c81d3e3cd,citation,http://pdfs.semanticscholar.org/beea/df57a976f23f4fd6fa8a330eac6c81d3e3cd.pdf,ESGM : Event Enrichment and Summarization by Graph Model,2015
-52,YFCC100M,yfcc_100m,43.614386,7.071125,EURECOM,edu,beeadf57a976f23f4fd6fa8a330eac6c81d3e3cd,citation,http://pdfs.semanticscholar.org/beea/df57a976f23f4fd6fa8a330eac6c81d3e3cd.pdf,ESGM : Event Enrichment and Summarization by Graph Model,2015
-53,YFCC100M,yfcc_100m,31.2284923,121.40211389,East China Normal University,edu,beeadf57a976f23f4fd6fa8a330eac6c81d3e3cd,citation,http://pdfs.semanticscholar.org/beea/df57a976f23f4fd6fa8a330eac6c81d3e3cd.pdf,ESGM : Event Enrichment and Summarization by Graph Model,2015
-54,YFCC100M,yfcc_100m,38.2530945,140.8736593,Tohoku University,edu,171042ba12818238e3c0994ff08d71f8c28d4134,citation,http://pdfs.semanticscholar.org/1710/42ba12818238e3c0994ff08d71f8c28d4134.pdf,Learning to Describe E-Commerce Images from Noisy Online Data,2016
-55,YFCC100M,yfcc_100m,42.4505507,-76.4783513,Cornell University,edu,8a8861ad6caedc3993e31d46e7de6c251a8cda22,citation,https://arxiv.org/pdf/1706.01869.pdf,StreetStyle: Exploring world-wide clothing styles from millions of photos,2017
-56,YFCC100M,yfcc_100m,47.6423318,-122.1369302,Microsoft,company,19d1855e021561d6da9d0200bb18e47f51cddda6,citation,https://arxiv.org/pdf/1604.03968.pdf,Visual Storytelling,2016
-57,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,19d1855e021561d6da9d0200bb18e47f51cddda6,citation,https://arxiv.org/pdf/1604.03968.pdf,Visual Storytelling,2016
-58,YFCC100M,yfcc_100m,42.3583961,-71.09567788,MIT,edu,0ae80aa149764e91544bbe45b80bb50434e7bda9,citation,http://pdfs.semanticscholar.org/714c/21c575d2c02a51f2dd5250164f1269be44ca.pdf,Ambient Sound Provides Supervision for Visual Learning,2016
-59,YFCC100M,yfcc_100m,47.6423318,-122.1369302,Microsoft,company,30193451e552286645baa00db7dcd05780d9e1da,citation,https://pdfs.semanticscholar.org/3019/3451e552286645baa00db7dcd05780d9e1da.pdf,On Available Corpora for Empirical Methods in Vision & Language,2015
-60,YFCC100M,yfcc_100m,42.3504253,-71.10056114,Boston University,edu,16815ef660ef9e4091a81044d430591348df72ee,citation,http://pdfs.semanticscholar.org/1681/5ef660ef9e4091a81044d430591348df72ee.pdf,Combining Texture and Shape Cues for Object Recognition with Minimal Supervision,2016
-61,YFCC100M,yfcc_100m,37.4102193,-122.05965487,Carnegie Mellon University,edu,2a2fd2538e19652721bc664f92056fbd08c604fd,citation,http://pdfs.semanticscholar.org/5042/096e3a80b14a6686014f338e0643f5270e65.pdf,Surveillance Video Analysis with External Knowledge and Internal Constraints,2016
-62,YFCC100M,yfcc_100m,38.0333742,-84.5017758,University of Kentucky,edu,4576b59a44f75120f6a2d17a4e9c52e894297661,citation,https://pdfs.semanticscholar.org/4576/b59a44f75120f6a2d17a4e9c52e894297661.pdf,Learning Geo-Temporal Image Features,2018
-63,YFCC100M,yfcc_100m,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016
-64,YFCC100M,yfcc_100m,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016
-65,YFCC100M,yfcc_100m,33.5934539,130.3557837,Information Technologies Institute,edu,7f05df12dff3defee495507abd4870a0a30c3590,citation,http://pdfs.semanticscholar.org/7f05/df12dff3defee495507abd4870a0a30c3590.pdf,Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features,2016
-66,YFCC100M,yfcc_100m,39.65404635,-79.96475355,West Virginia University,edu,b7b421be7c1dcbb8d41edb11180ba6ec87511976,citation,https://arxiv.org/pdf/1805.00324.pdf,A Deep Face Identification Network Enhanced by Facial Attributes Prediction,2018
-67,YFCC100M,yfcc_100m,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,7fda1edac608bc67e55ac3d7c9dc5a542d8f8aee,citation,http://pdfs.semanticscholar.org/b742/8da870a9872ecdaa6feaaab43c0bcd136dd2.pdf,Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding,2016