In the process of training Neural Networks, pre-training is an unsupervised training process that uses automatically generated labels for real end-goal task inputs. It usually precedes a supervised training stage, can improve neural network performance, and can reduce training loss. In this work, we used pre-training in the automotive domain where the setup was composed of a camera aimed outside the vehicle and an eye tracking system observing the driver. Our pre-training process used images from the camera as input and eye gaze direction as the automatic generated label. The eye gaze pre-training goal was to initiate and thus improve disparity estimation networks.Selecting eye gaze as labels is the best of both worlds. On one hand, it is somewhat similar to supervised training. Labels are generated by humans, by drivers who have deep understanding of the scenes and driving situation. On the other hand, it is similar to unsupervised training. The labels can be generated automatically. Large quantities of data can be collected easily. Overall, the eye gaze pre-train helped reduce the L1 loss from 0.65 when not using pre-train to 0.45 when using it on the validation set.

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)-pairs of points in source and target sets that are mutual nearest neighbours, i.e., each point is the nearest neighbour of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features.

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)-pairs of points in source and target sets, where each point is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset.

Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the object. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the object variations over time. The model is implemented using the Earth Mover's Distance (EMD) with two parameters that control the cost of moving pixels and changing their color. We adjust these costs on-line during tracking to account for the amount of local (dis)order in the object. We show LOT's tracking capabilities on challenging video sequences, both commonly used and new, demonstrating performance comparable to state-of-the-art methods.