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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
This paper considers fully connected CRF models defined on the complete set of pixels in an image and proposes a highly efficient approximate inference algorithm in which the pairwise edge potentials are defined by a linear combination of Gaussian kernels. Expand
Context Encoders: Feature Learning by Inpainting
It is found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures, and can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods. Expand
Saliency filters: Contrast based filtering for salient region detection
A conceptually clear and intuitive algorithm for contrast-based saliency estimation that outperforms all state-of-the-art approaches and can be formulated in a unified way using high-dimensional Gaussian filters. Expand
Objects as Points
The center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors and performs competitively with sophisticated multi-stage methods and runs in real-time. Expand
Adversarial Feature Learning
Bidirectional Generative Adversarial Networks are proposed as a means of learning the inverse mapping of GANs, and it is demonstrated that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning. Expand
Sampling Matters in Deep Embedding Learning
This paper proposes distance weighted sampling, which selects more informative and stable examples than traditional approaches, and shows that a simple margin based loss is sufficient to outperform all other loss functions. Expand
Geodesic Object Proposals
An approach for identifying a set of candidate objects in a given image that can be used for object recognition, segmentation, and other object-based image parsing tasks is presented. Expand
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
This work proposes Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space of a CNN, and demonstrates the generality of this new learning framework. Expand
Tracking Objects as Points
Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by pipelines thatExpand
Generative Visual Manipulation on the Natural Image Manifold
This paper proposes to learn the natural image manifold directly from data using a generative adversarial neural network, and defines a class of image editing operations, and constrain their output to lie on that learned manifold at all times. Expand