problem. The minimum cost multicut problem is first converted to an

unconstrained binary cubic formulation where cycle consistency constraints are

incorporated into the objective function. The new optimization problem can be

viewed as a Conditional Random Field (CRF) in which the random variables are

associated with the binary edge labels of the initial graph and the hard

constraints are introduced in the CRF as high-order potentials. The parameters

of a standard Neural Network and the fully differentiable CRF are optimized in

an end-to-end manner. Furthermore, our method utilizes the cycle constraints as

meta-supervisory signals during the learning of the deep feature

representations by taking the dependencies between the output random variables

into account. We present analyses of the end-to-end learned representations,

showing the impact of the joint training, on the task of clustering images of

MNIST. We also validate the effectiveness of our approach both for the feature

learning and the final clustering on the challenging task of real-world

multi-person pose estimation.

},\n}\n'