mtybilly/Instruct2DS huggingface dataset:
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Authors: Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, Jason Yosinski Anh Nguyen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize novel images by performing gradient descent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network.  In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models "Plug and Play Generative Networks". PPGNs are composed of (1) a generator network G that is capable of drawing a wide range of image types and (2) a replaceable "condition" network C that tells the generator what to draw. We demonstrate generation of images conditioned on a class - when C is an ImageNet classification network - and also conditioned on a caption - when C is an image captioning network. Our method also improves the state of the art of Deep Multifaceted Feature Visualization, which involves synthetically generating the set of inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While we operate on images in this paper, the approach is modality agnostic and can be applied to many types of data.
Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors
Authors: Yusuf Tas, Fatih Porikli Piotr Koniusz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: In this paper, we propose an approach to the domain adaptation, dubbed Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second- or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second- or even higher-order scatter tensors; one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains) and RGB-D combined with Caltech256 (depth-to-rgb transfer). We attain state-of-the-art results.
A Generative Model for Depth-Based Robust 3D Facial Pose Tracking
Authors: Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan Lu Sheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or data-driven methods that require sophisticated training or manual intervention, we propose a generative framework that unifies pose tracking and face model adaptation on-the-fly. Particularly, we propose a statistical 3D face model that owns the flexibility to generate and predict the distribution and uncertainty underlying the face model. Moreover, unlike prior arts employing the ICP-based facial pose estimation, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility against the input point cloud, which augments the robustness against the occlusions. The experimental results on Biwi and ICT-3DHP datasets reveal that the proposed framework is effective and outperforms the state-of-the-art depth-based methods.
Single Image Reflection Suppression
Authors: Radhakrishna Achanta, Sabine Susstrunk Nikolaos Arvanitopoulos
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Reflections are a common artifact in images taken through glass windows. Automatically removing the reflection artifacts after the picture is taken is an ill-posed problem. Attempts to solve this problem using optimization schemes therefore rely on various prior assumptions from the physical world. Instead of removing reflections from a single image, which has met with limited success so far, we propose a novel approach to suppress reflections. It is based on a Laplacian data fidelity term and an l-zero gradient sparsity term imposed on the output. With experiments on artificial and real-world images we show that our reflection suppression method performs better than the state-of-the-art reflection removal techniques.
Learning Non-Maximum Suppression
Authors: Rodrigo Benenson, Bernt Schiele Jan Hosang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, fea tures, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-maximum suppression (NMS), a post-processing algorithm responsible for merging all detections that belong to the same object. The de facto standard NMS algorithm is still fully hand-crafted, suspiciously simple, and -- being based on greedy clustering with a fixed distance threshold -- forces a trade-off between recall and precision. We propose a new network architecture designed to perform NMS, using only boxes and their score. We report experiments for person detection on PETS and for general object categories on the COCO dataset. Our approach shows promise providing improved localization and occlusion handling.
BRISKS: Binary Features for Spherical Images on a Geodesic Grid
Authors: William A. P. Smith Hao Guan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: In this paper, we develop an interest point detector and binary feature descriptor for spherical images. We take as inspiration a recent framework developed for planar images, BRISK (Binary Robust Invariant Scalable Keypoints), and adapt the method to operate on spherical images. All of our processing is intrinsic to the sphere and avoids the distortion inherent in storing and indexing spherical images in a 2D representation. We discretise images on a spherical geodesic grid formed by recursive subdivision of a triangular mesh. This leads to a multiscale pixel grid comprising mainly hexagonal pixels that lends itself naturally to a spherical image pyramid representation. For interest point detection, we use a variant of the Accelerated Segment Test (AST) corner detector which operates on our geodesic grid. We estimate a continuous scale and location for features and descriptors are built by sampling onto a regular pattern in the tangent space. We evaluate repeatability, precision and recall on both synthetic spherical images with known ground truth correspondences and real images.
Gaze Embeddings for Zero-Shot Image Classification
Authors: Zeynep Akata, Bernt Schiele, Andreas Bulling Nour Karessli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership. We present a data collection paradigm that involves a discrimination task to increase the information content obtained from gaze data. Our method extracts discriminative descriptors from the data and learns a compatibility function between image and gaze using three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid (GFG) and Gaze Features with Sequence (GFS). We introduce two new gaze-annotated datasets for fine-grained image classification and show that human gaze data is indeed class discriminative, provides a competitive alternative to expert-annotated attributes, and outperforms other baselines for zero-shot image classification.
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
Authors: Jing Liu, Chang Wen Chen Shuang Ma
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images is impaired because of potential loss of fine grained details and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an image's aesthetics. In this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This novel scheme is able to accept arbitrary sized images, and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated double-subnet neural network structure, i.e. a Multi-Patch subnet and a Layout-Aware subnet. We further construct an aggregation layer to effectively combine the hybrid features from these two subnets. Extensive experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.
Toroidal Constraints for Two-Point Localization Under High Outlier Ratios
Authors: Torsten Sattler, Andrea Cohen, Andreas Geiger, Marc Pollefeys Federico Camposeco
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Localizing a query image against a 3D model at large scale is a hard problem, since 2D-3D matches become more and more ambiguous as the model size increases. This creates a need for pose estimation strategies that can handle very low inlier ratios. In this paper, we draw new insights on the geometric information available from the 2D-3D matching process. As modern descriptors are not invariant against large variations in viewpoint, we are able to find the rays in space used to triangulate a given point that are closest to a query descriptor. It is well known that two correspondences constrain the camera to lie on the surface of a torus. Adding the knowledge of direction of triangulation, we are able to approximate the position of the camera from two matches alone. We derive a geometric solver that can compute this position in under 1 microsecond. Using this solver, we propose a simple yet powerful outlier filter which scales quadratically in the number of matches. We validate the accuracy of our solver and demonstrate the usefulness of our method in real world settings.
Hidden Layers in Perceptual Learning
Authors: Daphna Weinshall Gad Cohen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Studies in visual perceptual learning investigate the way human performance improves with practice, in the context of relatively simple (and therefore more manageable) visual tasks. Building on the powerful tools currently available for the training of Convolution Neural Networks (CNN), networks whose original architecture was inspired by the visual system, we revisited some of the open computational questions in perceptual learning. We first replicated two representative sets of perceptual learning experiments by training a shallow CNN to perform the relevant tasks. These networks qualitatively showed most of the characteristic behavior observed in perceptual learning, including the hallmark phenomena of specificity and its various manifestations in the forms of transfer or partial transfer, and learning enabling. We next analyzed the dynamics of weight modifications in the networks, identifying patterns which appeared to be instrumental for the transfer (or generalization) of learned skills from one task to another in the simulated networks. These patterns may identify ways by which the domain of search in the parameter space during network re-training can be significantly reduced, thereby accomplishing knowledge transfer.
InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation
Authors: Lior Wolf Shay Zweig
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Sparse-to-dense interpolation for optical flow is a fundamental phase in the pipeline of most of the leading optical flow estimation algorithms. The current state-of-the-art method for interpolation, EpicFlow, is a local average method based on an edge aware geodesic distance. We propose a new data-driven sparse-to-dense interpolation algorithm based on a fully convolutional network. We draw inspiration from the filling-in process in the visual cortex and introduce lateral dependencies between neurons and multi-layer supervision into our learning process. We also show the importance of the image contour to the learning process. Our method is robust and outperforms EpicFlow on competitive optical flow benchmarks with several underlying matching algorithms. This leads to state-of-the-art performance on the Sintel and KITTI 2012 benchmarks.
Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images
Authors: Junwei Han, Yang Yang, Dong Huang Dingwen Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Recently, researchers have made great processes to build category-specific 3D shape models from 2D images with manual annotations consisting of class labels, keypoints, and ground truth figure-ground segmentations. However, the annotation of figure-ground segmentations is still labor-intensive and time-consuming. To further alleviate the burden of providing such manual annotations, we make the earliest effort to learn category-specific 3D shape models by only using weakly labeled 2D images. By revealing the underlying relationship between the tasks of common object segmentation and category-specific 3D shape reconstruction, we propose a novel framework to jointly solve these two problems along a cluster-level learning curriculum. Comprehensive experiments on the challenging PASCAL VOC benchmark demonstrate that the category-specific 3D shape models trained using our weakly supervised learning framework could, to some extent, approach the performance of the state-of-the-art methods using expensive manual segmentation annotations. In addition, the experiments also demonstrate the effectiveness of using 3D shape models for helping common object segmentation.
Zero-Shot Learning - the Good, the Bad and the Ugly
Authors: Bernt Schiele, Zeynep Akata Yongqin Xian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss limitations of the current status of the area which can be taken as a basis for advancing it.
Learning the Multilinear Structure of Visual Data
Authors: Yannis Panagakis, Patrick Snape, Stefanos Zafeiriou Mengjiao Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Statistical decomposition methods are of paramount importance in discovering the modes of variations of visual data. Probably the most prominent linear decomposition method is the Principal Component Analysis (PCA), which discovers a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity, expression, pose etc. To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces, that rely on multilinear (tensor) decomposition (e.g., Higher Order SVD) have been developed. The main drawbacks of such methods is that they require both labels regarding the modes of variations and the same number of samples under all modes of variations (e.g., the same face under different expressions, poses etc.). Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. In this paper, we propose the first general multilinear method, to the best of our knowledge, that discovers the multilinear structure of visual data in unsupervised setting. That is, without the presence of labels. We demonstrate the applicability of the proposed method in two applications, namely Shape from Shading (SfS) and expression transfer.
Linking Image and Text With 2-Way Nets
Authors: Lior Wolf Aviv Eisenschtat
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Linking two data sources is a basic building block in numerous computer vision problems.  Canonical Correlation Analysis (CCA) achieves this by utilizing a linear optimizer in order to maximize the correlation between the two views.  Recent work makes use of non-linear models, including deep learning techniques, that optimize the CCA loss in some feature space. In this paper, we introduce a novel, bi-directional neural network architecture for the task of matching vectors from two data sources. Our approach employs two tied neural network channels that project the two views into a common, maximally correlated space using the Euclidean loss. We show a direct link between the correlation-based loss and Euclidean loss, enabling the use of Euclidean loss for correlation maximization. To overcome common Euclidean regression optimization problems, we modify well-known techniques to our problem, including batch normalization and dropout. We show state of the art results on a number of computer vision matching tasks including MNIST image matching and sentence-image matching on the Flickr8k, Flickr30k and COCO datasets.
Unsupervised Semantic Scene Labeling for Streaming Data
Authors: John G. Rogers III Maggie Wigness
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream. While closely related to unsupervised video segmentation, our algorithm is not designed to be an early video processing strategy that produces coherent over-segmentations, but instead, to directly learn higher-level semantic concepts. This is achieved with an ensemble-based approach, where each learner clusters data from a local window in the data stream. Overlapping local windows are processed and encoded in a graph structure to create a label mapping across windows and reconcile the labelings to reduce unsupervised learning noise. Additionally, we iteratively learn a merging threshold criteria from observed data similarities to automatically determine the number of learned labels without human provided parameters. Experiments show that our approach semantically labels video streams with a high degree of accuracy, and achieves a better balance of under and over-segmentation entropy than existing video segmentation algorithms given similar numbers of label outputs.
Fast-At: Fast Automatic Thumbnail Generation Using Deep Neural Networks
Authors: Bharat Singh, Larry S. Davis Seyed A. Esmaeili
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a  fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search over the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques.
3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder
Authors: Tamar Avraham, Anath Fischer Gil Elbaz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points--local subsets of points--and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders.   Abstract This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise, and missing data are shown.
Improved Stereo Matching With Constant Highway Networks and Reflective Confidence Learning
Authors: Lior Wolf Amit Shaked
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches. A novel post-processing step is then introduced, which employs a second deep convolutional neural network for pooling global information from multiple disparities. This network outputs both the image disparity map, which replaces the conventional "winner takes all" strategy, and a confidence in the prediction. The confidence score is achieved by training the network with a new technique that we call the reflective loss. Lastly, the learned confidence is employed in order to better detect outliers in the refinement step. The proposed pipeline achieves state of the art accuracy on the largest and most competitive stereo benchmarks, and the learned confidence is shown to outperform all existing alternatives.
Superpixels and Polygons Using Simple Non-Iterative Clustering
Authors: Sabine Susstrunk Radhakrishna Achanta
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.
POSEidon: Face-From-Depth for Driver Pose Estimation
Authors: Marco Venturelli, Roberto Vezzani, Rita Cucchiara Guido Borghi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses.  In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regressive neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth.  In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth model for learning image faces from depth.  Results in face reconstruction are qualitatively impressive.  We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.
Optical Flow in Mostly Rigid Scenes
Authors: Laura Sevilla-Lara, Michael J. Black Jonas Wulff
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Authors: Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi Christian Ledig
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.
ROAM: A Rich Object Appearance Model With Application to Rotoscoping
Authors: Juan-Manuel Perez-Rua, Philip H. S. Torr, Patrick Perez Ondrej Miksik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic segmentation with a closed curve or pixel-wise binary labelling.
Densely Connected Convolutional Networks
Authors: Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger Gao Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections--one between each layer and its subsequent layer--our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR- 10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.
Multi-Level Attention Networks for Visual Question Answering
Authors: Jianlong Fu, Tao Mei, Yong Rui Dongfei Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Inspired by the recent success of text-based question answering, visual question answering (VQA) is proposed to automatically answer natural language questions with the reference to a given image. Compared with text-based QA, VQA is more challenging because the reasoning process on visual domain needs both effective semantic embedding and fine-grained visual understanding. Existing approaches predominantly infer answers from the abstract low-level visual features, while neglecting the modeling of high-level image semantics and the rich spatial context of regions. To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention. First, we generate semantic concepts from high-level semantics in convolutional neural networks (CNN) and select those question-related concepts as semantic attention. Second, we encode region-based middle-level outputs from CNN into spatially-embedded representation by a bidirectional recurrent neural network, and further pinpoint the answer-related regions by multiple layer perceptron as visual attention. Third, we jointly optimize semantic attention, visual attention and question embedding by a softmax classifier to infer the final answer. Extensive experiments show the proposed approach outperforms the-state-of-arts on two challenging VQA datasets.
Spatial-Semantic Image Search by Visual Feature Synthesis
Authors: Hailin Jin, Zhe Lin, Chen Fang, Jonathan Brandt, Feng Liu Long Mai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: The performance of image retrieval has been improved tremendously in recent years through the use of deep feature representations. Most existing methods, however, aim to retrieve images that are visually similar or semantically relevant to the query, irrespective of spatial configuration. In this paper, we develop a spatial-semantic image search technology that enables users to search for images with both semantic and spatial constraints by manipulating concept text-boxes on a 2D query canvas. We train a convolutional neural network to synthesize appropriate visual features that captures the spatial-semantic constraints from the user canvas query. We directly optimize the retrieval performance of the visual features when training our deep neural network. These visual features then are used to retrieve images that are both spatially and semantically relevant to the user query. The experiments on large-scale datasets such as MS-COCO and Visual Genome show that our method outperforms other baseline and state-of-the-art methods in spatial-semantic image search.
Temporal Residual Networks for Dynamic Scene Recognition
Authors: Axel Pinz, Richard P. Wildes Christoph Feichtenhofer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: This paper combines three contributions to establish a new state-of-the-art in dynamic scene recognition. First, we present a novel ConvNet architecture based on temporal residual units that is fully convolutional in spacetime. Our model augments spatial ResNets with convolutions across time to hierarchically add temporal residuals as the depth of the network increases. Second, existing approaches to video-based recognition are categorized and a baseline of seven previously top performing algorithms is selected for comparative evaluation on dynamic scenes. Third, we introduce a new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. Our evaluations verify the particular strengths and weaknesses of the baseline algorithms with respect to various scene classes and camera motion parameters. Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.
Radiometric Calibration for Internet Photo Collections
Authors: Boxin Shi, Sai-Kit Yeung, Yasuyuki Matsushita Zhipeng Mo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Radiometrically calibrating the images from Internet photo collections brings photometric analysis from lab data to big image data in the wild, but conventional calibration methods cannot be directly applied to such image data. This paper presents a method to jointly perform radiometric calibration for a set of images in an Internet photo collection. By incorporating the consistency of scene reflectance for corresponding pixels in multiple images, the proposed method estimates radiometric response functions of all the images using a rank minimization framework. Our calibration aligns all response functions in an image set up to the same exponential ambiguity in a robust manner. Quantitative results using both synthetic and real data show the effectiveness of the proposed method.
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification
Authors: Yan Huang, Wei Wang, Liang Wang, Tieniu Tan Zhen Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. This topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. The proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. That is, our method handles spatial and temporal information simultaneously in a unified manner. The carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.
Procedural Generation of Videos to Train Deep Action Recognition Networks
Authors: Adrien Gaidon, Yohann Cabon, Antonio Manuel Lopez Cesar Roberto de Souza
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos". It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
Spatiotemporal Multiplier Networks for Video Action Recognition
Authors: Axel Pinz, Richard P. Wildes Christoph Feichtenhofer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: This paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features. Our model combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end. We theoretically motivate multiplicative gating functions for residual networks and empirically study their effect on classification accuracy. To capture long-term dependencies we inject  identity mapping kernels for learning temporal relationships. Our architecture is fully convolutional in spacetime and able to evaluate a video in a single forward pass. Empirical investigation reveals that our model produces state-of-the-art results on two standard action recognition datasets.
Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation
Authors: Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi Jose Caballero
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution have been limited to naive or inefficient architectures. In this paper, we introduce spatio-temporal sub-pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed. Specifically, we discuss the use of early fusion, slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames. We also propose a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable. These contributions provide both higher accuracy and temporally more consistent videos, which we confirm qualitatively and quantitatively. Relative to single-frame models, spatio-temporal networks can either reduce the computational cost by 30% whilst maintaining the same quality or provide a 0.2dB gain for a similar computational cost. Results on publicly available datasets demonstrate that the proposed algorithms surpass current state-of-the-art performance in both accuracy and efficiency.
Query-Focused Video Summarization: Dataset, Evaluation, and a Memory Network Based Approach
Authors: Jacob S. Laurel, Boqing Gong Aidean Sharghi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity --- users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer.   To tackle the first problem, we explore the recently proposed query-focused video summarization which  introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient  evaluation method defined upon the  concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.