This paper investigates how to extract objects-of-interest without relying on
hand-craft features and sliding windows approaches, that aims to jointly solve
two sub-tasks: (i) rapidly localizing salient objects from images, and (ii)
accurately segmenting the objects based on the localizations. We present a
general joint task learning framework, in which each task (either object
localization or object segmentation) is tackled via a multi-layer convolutional
neural network, and the two networks work collaboratively to boost performance.
In particular, we propose to incorporate latent variables bridging the two
networks in a joint optimization manner. The first network directly predicts
the positions and scales of salient objects from raw images, and the latent
variables adjust the object localizations to feed the second network that
produces pixelwise object masks. An EM-type method is presented for the
optimization, iterating with two steps: (i) by using the two networks, it
estimates the latent variables by employing an MCMC-based sampling method; (ii)
it optimizes the parameters of the two networks unitedly via back propagation,
with the fixed latent variables. Extensive experiments suggest that our
framework significantly outperforms other state-of-the-art approaches in both
accuracy and efficiency (e.g. 1000 times faster than competing approaches).

本文提出了一种用于从图像中快速定位显着对象并基于定位精确分割对象的方法，该方法采用多层卷积神经网络框架，并使用潜变量在联合优化方面进行协作，取得了比其他最先进的方法更高的准确性和效率。