- Shoou-I Yu, Lu Jiang, Zhongwen Xu, Zhenzhong Lan, Shicheng Xu, Xiaojun Chang, Xuanchong Li, Zexi Mao, Chuang Gan, Yajie Miao, Xingzhong Du, Yang Cai, Lara Martin, Nikolas Wolfe, Anurag Kumar, Hua Li, Ming Lin, Zhigang Ma, Yi Yang, Deyu Meng, Shiguang Shan, Pinar Duygulu Sahin, Susanne Burger, Florian Metze, Rita Singh, Bhiksha Raj, Teruko Mitamura, Richard Stern, Alexander Hauptmann. 2015. CMU Informedia@TRECVID 2015: MED/SIN/LNK/SED. Abstract: We report on our system used in the TRECVID 2015 Multimedia Event Detection (MED) task. On the MED task, the CMU team submitted runs in the Semantic Query (SQ) and 10Ex settings. The proposed system is essentially the same as our MED 2014 system. 1.1 MED System Description On the MED task, the CMU team uses the MED 2014 [1] system which has enabled the system to achieve good performance in the 000Ex and 010Ex settings. Furthermore, our system is very efficient in that it can complete Event Query Generation (EQG) in 16 minutes and Event Search (ES) over 200,000 videos in less than 5 minutes on a single workstation. Please see [1] for the detailed system for the details about our 000Ex and 010Ex runs. The CMU system utilizes multiple modalities, classifiers and fusion methods to perform Multimedia Event Detection. The multiple modalities include visual, audio and text modalities. For 10Ex, two classifiers were used: linear SVM and linear regression. The fusion method used for 010Ex is the Multistage Hybrid Late Fusion, which is a combination of many different fusion algorithms. For the 000Ex runs, we utilize concept detection results from 3000 concept detectors during the SQG and ES stage. We submitted four runs for this year's PS condition: CMU_MED15_MED15EvalFull_PS_10Ex_MED_p-baseline_1: The baseline 10Ex system similar to our 2014 system (using same set of features). CMU_MED15_MED15EvalFull_PS_0Ex_MED_p-expert_1: The 0Ex system using the manual queries selected by experts (using the same queries in our 2014 system). CMU_MED15_MED15EvalFull_PS_0Ex_MED_c-autosqg_1: The 0Ex system using the automatically generated queries. The automatic query generation process is detailed in [3]. CMU_MED15_MED15EvalFull_PS_0Ex_MED_c-autosqgvisualonly_1: The 0Ex system using the automatically generated queries in [3] with only visual features (not including ASR and OCR). 1.2 Hardware Description We utilize the following hardware for metadata generation: 1. PSC Blacklight cluster 100 nodes, each with 4 Intel(R) Xeon(R) CPU E5620 2.40 GHz CPUs (4 cores), 128 GB RAM. Lustre fistributed filesystem, where we used around 50TB. 2. Rocks cluster 20 nodes, each with 2 Intel XEON E5649 2.53 GHz CPUs (6 cores), 64 GB RAM 4 nodes, each with 4 Intel XEON E5-2660 2.20 GHz CPUs (8 cores), 128 GB RAM. 3 nodes, each with 4 NVIDIA TESLA K20 GPUs. 2 data servers, 30TB each For 10Ex event search, we use: 1 Intel(R) Xeon(R) CPU E5-2640 2.50 GHz CPU (12 cores), 128GB RAM, 4 NVIDIA TESLA K20s (2496 cores each), SSD RAID with 4TB storage. For 0Ex event search, we use: 1 Intel(R) Xeon(R) CPU E5649 @ 2.53GHz, 64GB RAM, with a 256GB non-SSD Hard Disk. 1.3 System Performance We report our performance on MED15EvalFull Pre-Specified Events. Runs (MED15EvalFull) Performance MAP% iP10 iP50 infAP200 000Ex autosqgvisualonly 6.4 0.13 0.135 0.0611 000Ex autosqg 7.8 0.2 0.188 0.1005 000Ex expert 15.1 0.38 0.307 0.2137 010Ex baseline 19.2 0.495 0.394 0.2376 Runs (MED15EvalSub) Performance MAP% iP10 iP50 infAP200 000Ex autosqgvisualonly 9.7 0.19 0.148 0.0895 000Ex autosqg 11.0 0.245 0.174 0.1223 000Ex expert 20.6 0.39 0.285 0.246 010Ex baseline 25.5 0.515 0.343 0.2882 Acknowledgments This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. Specifically, it used the Blacklight system at the Pittsburgh Supercomputing Center (PSC). This work was supported in part by the US Department of Defense, U. S. Army Research Office (W911NF-13-1-0277) and by the National Science Foundation under Grant No. IIS-1251187. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ARO, the National Science Foundation or the U.S. Government. 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