title: Inducing Stronger Object Representations in Deep Visual Trackers
abstract: Fully convolutional deep correlation networks are integral components of state-of-
  the-art approaches to single object visual tracking. It is commonly assumed that
  these networks perform tracking by detection by matching features of the object
  instance with features of the entire frame. Strong architectural priors and conditioning
  on the object representation is thought to encourage this tracking strategy.
  Despite these strong priors, we show that deep trackers often default to “tracking-
  by-saliency” detection – without relying on the object instance representation. Our
  analysis shows that despite being a useful prior, salience detection can prevent the
  emergence of more robust tracking strategies in deep networks. This leads us to
  introduce an auxiliary detection task that encourages more discriminative object
  representations that improve tracking performance.
authors: Ross Goroshin Jonathan Tompson Debidatta Dwibedi
hIndex: Jonathan Tompson 24