Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains

Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial pooling to achieve scale and shift invariances, but they are still sensitive to out-of-plane rotations. In this paper, we formulate a probabilistic framework for analyzing the performance of pooling. This framework suggests two directions for improvement. First, we apply multiple scales of filters coupled with different pooling granularities, and second we make use of color as an additional pooling domain, thereby reducing the sensitivity to spatial deformations. We evaluate our algorithm on the object instance recognition task using two independent publicly available RGB-D datasets, and demonstrate significant improvements over the current state-of-the-art. In addition, we present a new dataset for industrial objects to further validate the effectiveness of our approach versus other state-of-the-art approaches for object recognition using RGB-D data.