Object based Scene Representations using Fisher Scores of Local Subspace Projections

Several works have shown that deep CNNs can be easily transferred across datasets, e.g. the transfer from object recognition on ImageNet to object detection on Pascal VOC. Less clear, however, is the ability of CNNs to transfer knowledge across tasks. A common example of such transfer is the problem of scene classification, that should leverage localized object detections to recognize holistic visual concepts. While this problems is currently addressed with Fisher vector representations, these are now shown ineffective for the high-dimensional and highly non-linear features extracted by modern CNNs. It is argued that this is mostly due to the reliance on a model, the Gaussian mixture of diagonal covariances, which has a very limited ability to capture the second order statistics of CNN features.