52,922 frames are utilized to train the model.
We initialize two models with original VGG-16 and other two models with retrained one.Jian, bing, man ( 2015 ).Smeulders International Journal of Computer Vision, Volume 104 (2 page 154-171, 2013 GatorVision Dave Ojika, University of Florida Liu Chujia, University of Florida Rishab Goel, University of Florida Vivek Viswanath, University of Florida Arpita Tugave, University of Florida Shruti Sivakumar, University of Florida Dapeng.For each test image we predict the five highest scoring scenes.Using those, we train an inception-style convolutional neural network.Torr, bing: Binarized normed gradients for objectness estimation at 300fps, in Proc.The region proposal algorithm EdgeBoxes 2 is employed to generate region of interests from a frame, and features are generated using 16 layer CNN network 3 which pre-trained on the ilsvrc 2013 CLS dataset and fine-tuned on the ilsvrc 2015 video dataset.1Sergey Ioffe, Christian Szegedy.Combine Information and Pair Select So far, we have got objectness and offsets regression for some boxes, classification results for both local and global.The final models achieve.9 top-5 cls-loc error and.62 cls error on the validation set.
We subsample 15K object categories from the 22K ImageNet dataset, for which more than 200 training examples are available.
The second model is the one mentioned in Ref.
For submission, we submit results of each model as the first three runs (run 1, run 2, and run 3).
The average operation is done after the softmax calculation.
Kaistnia_etri Hyungwon Choi kaist) Yunhun Jang kaist) Keun Dong Lee etri) Seungjae Lee etri) Jinwoo Shin kaist) indexes musical jackpot liedjes equal contribution, by Alphabets) In this work, we use a variant of GoogLenet 1 for localization task.
We used pre-trained Imagenet 2014 classification models (VGG16, VGG19) to train detection models.It is worth noting that we set most of parameters empirically because we have no time to validate them.Thomas Unterthiner (Institute of Bioinformatics, Johannes Kepler University Linz).Some models were trained by maintaining the aspect ratio of input images, while others were not.The above characteristics of epitome encourage us to arrange filters in a way similar to epitome in the fpcnns.
Sysu_Vision Liang Lin, Sun Yat-sen University Wenxi Wu, Sun Yat-sen University Zhouxia Wang, Sun Yat-sen University Depeng Liang, Sun Yat-sen University Tianshui Chen, Sun Yat-sen University Xian Wu, Sun Yat-sen University Keze Wang, Sun Yat-sen University Lingbo Liu, Sun Yat-sen University We design our detection.
SenseTime Group Limited For object detection in video, we first employ CNN based on detectors to detect and classify candidate regions on individual frames.
It leads to 93 recall rate with about 126 proposals per image on val2.