Segmentation-Aware Convolutional Networks Using Local Attention Masks

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can match the performance of DenseCRFs while being faster and simpler, and in optical flow we obtain clearly sharper responses than networks that do not use local attention masks. In both cases, segmentation-aware convolution yields systematic improvements over strong baselines. Source code for this work is available online at http://cs.cmu.edu/~aharley/segaware

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

On the Use of Efficient Projection Kernels for Motion-Based Visual Saliency Estimation

In this paper, we investigate the potential of a family of efficient filters—the Gray-Code Kernels (GCKs)—for addressing visual saliency estimation with a focus on motion information. Our implementation relies on the use of 3D kernels applied to overlapping blocks of frames and is able to gather meaningful spatio-temporal information with a very light computation. We introduce an attention module that reasons the use of pooling strategies, combined in an unsupervised way to derive a saliency map highlighting the presence of motion in the scene. A coarse segmentation map can also be obtained. In the experimental analysis, we evaluate our method on publicly available datasets and show that it is able to effectively and efficiently identify the portion of the image where the motion is occurring, providing tolerance to a variety of scene conditions and complexities