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The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.
Signature verification using a “Siamese” time delay neural network
Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: · 1993
Earlier work this paper cites.
Struck: Structured output tracking with kernels
Hare, S., Saffari, A., Torr, P.H.S.: · 2011
Earlier work this paper cites.
Tracking-learning-detection
Kalal, Z., Mikolajczyk, K., Matas, J.: · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
Learning where to attend with deep architectures for image tracking
Denil, M., Bazzani, L., Larochelle, H., de Freitas, N.: · 2012
Earlier work this paper cites.
Online object tracking: A benchmark
Wu, Y., Lim, J., Yang, M.H.: · 2013
Earlier work this paper cites.
Visual tracking: An experimental survey
Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: · 2014
Earlier work this paper cites.
CNN features off-the-shelf: An astounding baseline for recognition
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: · 2014
Earlier work this paper cites.
DeepFace: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: · 2014
Earlier work this paper cites.
Accurate scale estimation for robust visual tracking
Danelljan, M., Häger, G., Khan, F., Felsberg, M.: · 2014
Earlier work this paper cites.
Ensemble-based tracking: Aggregating crowdsourced structured time series data
Wang, N., Yeung, D.Y.: · 2014
Earlier work this paper cites.
A scale adaptive kernel correlation filter tracker with feature integration
Li, Y., Zhu, J.: · 2014
Earlier work this paper cites.
High-speed tracking with kernelized correlation filters
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: · 2015
Earlier work this paper cites.
Hierarchical convolutional features for visual tracking
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: · 2015
Earlier work this paper cites.
Convolutional features for correlation filter based visual tracking
Danelljan, M., Hager, G., Khan, F., Felsberg, M.: · 2015
Earlier work this paper cites.
Transferring rich feature hierarchies for robust visual tracking
Wang, N., Li, S., Gupta, A., Yeung, D.Y.: · 2015
Cited alongside, same era.
Visual tracking with fully convolutional networks
Wang, L., Ouyang, W., Wang, X., Lu, H.: · 2015
Cited alongside, same era.
Learning multi-domain convolutional neural networks for visual tracking
Nam, H., Han, B.: · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Cited alongside, same era.
The Visual Object Tracking VOT2015 Challenge results
Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., Vojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: · 2015
Cited alongside, same era.
Deep face recognition
Parkhi, O.M., Vedaldi, A., Zisserman, A.: · 2015
Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Later among the works it cites.
Long-term correlation tracking
Ma, C., Yang, X., Zhang, C., Yang, M.H.: · 2015
Later among the works it cites.
Collaborative correlation tracking
Zhu, G., Wang, J., Wu, Y., Lu, H.: · 2015
Later among the works it cites.
Tracking randomly moving objects on edge box proposals
Zhu, G., Porikli, F., Li, H.: · 2015
Later among the works it cites.
Learning spatially regularized correlation filters for visual tracking
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: · 2015
Later among the works it cites.
Online object tracking with proposal selection
Hua, Y., Alahari, K., Schmid, C.: · 2015
Later among the works it cites.
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Cited alongside, same era.
FlowNet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: · 2015
Cited alongside, same era.
Learning to compare image patches via convolutional neural networks
Zagoruyko, S., Komodakis, N.: · 2015
Cited alongside, same era.
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., Philbin, J.: · 2015
Cited alongside, same era.
Discriminative learning of deep convolutional feature point descriptors
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., Salakhutdinov, R.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
In defense of color-based model-free tracking
Possegger, H., Mauthner, T., Bischof, H.: · 2015
Later among the works it cites.
Efficient deep learning for stereo matching
Luo, W., Schwing, A.G., Urtasun, R.: · 2016
Closest in time.
Learning to track at 100 fps with deep regression networks
Held, D., Thrun, S., Savarese, S.: · 2016
Closest in time.
Once for all: A two-flow convolutional neural network for visual tracking
Chen, K., Tao, W.: · 2016
Closest in time.
Siamese instance search for tracking
Tao, R., Gavves, E., Smeulders, A.W.M.: · 2016
Closest in time.
Staple: Complementary learners for real-time tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: · 2016
Closest in time.
Visual tracking using attention-modulated disintegration and integration
Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: · 2016
Closest in time.
Object tracking via dual linear structured svm and explicit feature map
Ning, J., Yang, J., Jiang, S., Zhang, L., Yang, M.H.: · 2016
Closest in time.
NUS-PRO: A new visual tracking challenge
Li, A., Lin, M., Wu, Y., Yang, M.H., Yan, S.: · 2016
Closest in time.