Tracking by Natural Language Specification
Reference: Z. Li, R. Tao, E. Gavves, C. G. M. Snoek, A. W. M. Smeulders, *Tracking by Natural Language Specification*, in Computer Vision and Pattern Recognition (CVPR), 2017
Abstract: This work strives to track a target object in a video. Rather than specifying the target in the first frame of a video by a bounding box, we propose to track the object based on a natural language specification of the target, which provides a more natural human-machine interaction as well as a means to improve tracking results. We define three variants of tracking by language specification: one relying on lingual target specification only, one relying on visual target specification based on language, and one leveraging their joint capacity. To show the potential of tracking by natural language specification we extend two popular tracking datasets with lingual descriptions and report experiments. Finally, we also sketch new tracking scenarios in surveillance and other live video streams that become feasible with a lingual specification of the target.
Siamese Instance Search for Tracking
Reference: Tao, Ran, Efstratios Gavves, and Arnold WM Smeulders. “Siamese instance search for tracking.” in Computer Vision and Pattern Recognition (CVPR), 2016.
Abstract: In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.
Sigma Delta Quantized Networks
Reference: O’Connor, Peter and Welling, Max “Sigma Delta Quantized Networks.” in International Conference on Learning Representations (ICLR), 2017.
Abstract: Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce Sigma-Delta networks. With each new input, each layer in this network sends a discretized form of its change in activation to the next layer. Thus the amount of computation that the network does scales with the amount of change in the input and layer activations, rather than the size of the network. We introduce an optimization method for converting any pre-trained deep network into an optimally efficient Sigma-Delta network, and show that our algorithm, if run on the appropriate hardware, could cut at least an order of magnitude from the computational cost of processing video data.
A Python tool for organizing your experiments
Peter & Matthias
A Java Library for building Event-Based Networks, callable from Python
Used in Deep Spiking Networks: https://arxiv.org/
Replicates the Experiments from Deep Spiking Networks: https://arxiv.org/abs/1602.
Replicates the Experiments from Sigma Delta Quantized Networks: http://openreview.
Proportional Derivative Neural Networks
Replicates the Experiments in Temporally Efficient Deep Learning with Spikes: https://arxiv.org/abs/