mtybilly/Instruct2DS huggingface dataset:
Deep Contrast Learning for Salient Object Detection
Authors: Guanbin Li, Yizhou Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.
Multiview Image Completion With Space Structure Propagation
Authors: Seung-Hwan Baek, Inchang Choi, Min H. Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with geometric consistency by creating or removing structures on the specified region. The proposed method incorporates photographs to estimate dense depth maps. We initially complete color as well as depth from a view, and then facilitate two stages of structure propagation and structure-guided completion. Structure propagation optimizes space topology in the scene across photographs, while structure-guide completion enhances, and completes local image structure of both depth and color in multiple photographs with structural coherence by searching nearest neighbor fields in relevant views. We demonstrate the effectiveness of the proposed method in completing multiview images.
Composition-Preserving Deep Photo Aesthetics Assessment
Authors: Long Mai, Hailin Jin, Feng Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Photo aesthetics assessment is challenging. Deep convolutional neural network (ConvNet) methods have recently shown promising results for aesthetics assessment. The performance of these deep ConvNet methods, however, 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, scaling, or padding, which often damages image composition, reduces image resolution, or causes image distortion, thus compromising the aesthetics of the original images. In this paper, we present a composition-preserving deep ConvNet method that directly learns aesthetics features from the original input images without any image transformations. Specifically, our method adds an adaptive spatial pooling layer upon the regular convolution and pooling layers to directly handle input images with original sizes and aspect ratios. To allow for multi-scale feature extraction, we develop the Multi-Net Adaptive Spatial Pooling ConvNet architecture which consists of multiple sub-networks with different adaptive spatial pooling sizes and leverage a scene-based aggregation layer to effectively combine the predictions from multiple sub-networks. Our experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate that our method can significantly improve the state-of-the-art results in photo aesthetics assessment.
Automatic Image Cropping : A Computational Complexity Study
Authors: Jiansheng Chen, Gaocheng Bai, Shaoheng Liang, Zhengqin Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Attention based automatic image cropping aims at preserving the most visually important region in an image. A common task in this kind of method is to search for the smallest rectangle inside which the summed attention is maximized. We demonstrate that under appropriate formulations, this task can be achieved using efficient algorithms with low computational complexity. In a  practically useful scenario where the aspect ratio of the cropping rectangle is given, the problem can be solved with a computational complexity linear to the number of image pixels. We also study the possibility of multiple rectangle cropping and a new model facilitating fully automated image cropping.
A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond
Authors: Neil D. B. Bruce, Christopher Catton, Sasa Janjic
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation. The overarching goal of the paper is to identify high level considerations relevant to deriving more sophisticated visual saliency models. A deep learning model based on fully convolutional networks (FCNs) is presented, which shows very favorable performance across a wide variety of benchmarks relative to existing proposals. We also demonstrate that the manner in which training data is selected, and ground truth treated is critical to resulting model behaviour. Recent efforts have explored the relationship between human gaze and salient objects, and we also examine this point further in the context of FCNs. Close examination of the proposed and alternative models serves as a vehicle for identifying problems important to developing more comprehensive models going forward.
Spatially Binned ROC: A Comprehensive Saliency Metric
Authors: Calden Wloka, John Tsotsos
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: A recent trend in saliency algorithm development is large-scale benchmarking and algorithm ranking with ground truth provided by datasets of human fixations. In order to accommodate the strong bias humans have toward central fixations, it is common to replace traditional ROC metrics with a shuffled ROC metric which uses randomly sampled fixations from other images in the database as the negative set. However, the shuffled ROC introduces a number of problematic elements, including a fundamental assumption that it is possible to separate visual salience and image spatial arrangement.  We argue that it is more informative to directly measure the effect of spatial bias on algorithm performance rather than try to correct for it. To capture and quantify these known sources of bias, we propose a novel metric for measuring saliency algorithm performance: the spatially binned ROC (spROC). This metric provides direct insight into the spatial biases of a saliency algorithm without sacrificing the intuitive raw performance evaluation of traditional ROC measurements. By quantitatively measuring the bias in saliency algorithms, researchers will be better equipped to select and optimize the most appropriate algorithm for a given task. We use a baseline measure of inherent algorithm bias to show that Adaptive Whitening Saliency (AWS) [14], Attention by Information Maximization (AIM) [8], and Dynamic Visual Attention (DVA) [20] provide the least spatially biased results, suiting them for tasks in which there is no information about the underlying spatial bias of the stimuli, whereas algorithms such as Graph Based Visual Saliency (GBVS) [18] and Context-Aware Saliency (CAS) [15] have a significant inherent central bias.
GraB: Visual Saliency via Novel Graph Model and Background Priors
Authors: Qiaosong Wang, Wen Zheng, Robinson Piramuthu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors. The input image is represented as an undirected graph with superpixels as nodes. Feature vectors are extracted from each node to cover regional color, contrast and texture information. A novel graph model is proposed to effectively capture local and global saliency cues. To obtain more accurate saliency estimations, we optimize the saliency map by using a robust background measure. Comprehensive evaluations on benchmark datasets indicate that our algorithm universally surpasses state-of-the-art unsupervised solutions and performs favorably against supervised approaches.
Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent
Authors: Anna Volokitin, Michael Gygli, Xavier Boix
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the tight link between visual attention and object recognition. In this paper, we show that using features from DCNNs for object recognition we can make predictions that enrich the information provided by saliency models. Namely, we can estimate the reliability of a saliency model from the raw image, which serves as a meta-saliency measure that may be used to select the best saliency algorithm for an image. Analogously, the consistency of the eye fixations among  subjects, i.e. the agreement between the eye fixation locations of different subjects, can also be predicted and used by a designer to assess whether subjects reach a consensus about salient image locations.
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer
Authors: Oriel Frigo, Neus Sabater, Julie Delon, Pierre Hellier
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques.
Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions
Authors: Lilian Calvet, Pierre Gurdjos, Carsten Griwodz, Simone Gasparini
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Using fiducial markers ensures reliable detection and identification of planar features in images. Fiducials are used in a wide range of applications, especially when a reliable visual reference is needed, e.g., to track the camera in cluttered or textureless environments. A marker designed for such applications must be robust to partial occlusions, varying distances and angles of view, and fast camera motions. In this paper, we present a robust, highly accurate fiducial system, whose markers consist of concentric rings, along with its theoretical foundations. Relying on projective properties, it allows to robustly localize the imaged marker and to accurately detect the position of the image of the (common) circle center. We demonstrate that our system can detect and accurately localize these circular fiducials under very challenging conditions and the experimental results reveal that it outperforms other recent fiducial systems.
Scene Recognition With CNNs: Objects, Scales and Dataset Bias
Authors: Luis Herranz, Shuqiang Jiang, Xiangyang Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN, showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the state-of-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).
Learning Action Maps of Large Environments via First-Person Vision
Authors: Nicholas Rhinehart, Kris M. Kitani
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate functional understanding of large spaces by leveraging activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in both large scenes where people have behaved, as well as novel scenes where no behaviors are available. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe demonstrations, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras, and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we take a preliminary look at the breadth of applicability of Action Maps by demonstrating a proof-of-concept application in which they are used in concert with activity detections to perform localization.
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Authors: Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, Yi Ma
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper aims to develop a method that can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end,we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.
Shallow and Deep Convolutional Networks for Saliency Prediction
Authors: Junting Pan, Elisa Sayrol, Xavier Giro-i-Nieto, Kevin McGuinness, Noel E. O'Connor
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Authors: Mohammad Najafi, Sarah Taghavi Namin, Mathieu Salzmann, Lars Petersson
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Scene parsing has attracted a lot of attention in computer vision. While parametric models have proven effective for this task, they cannot easily incorporate new training data. By contrast, nonparametric approaches, which bypass any learning phase and directly transfer the labels from the training data to the query images, can readily exploit new labeled samples as they become available. Unfortunately, because of the computational cost of their label transfer procedures, state-of-the-art nonparametric methods typically filter out most training images to only keep a few  relevant ones to label the query. As such, these methods throw away many images that still contain valuable information and generally obtain an unbalanced set of labeled samples. In this paper, we introduce a nonparametric approach to scene parsing that follows a sample-and-filter strategy. More specifically, we propose to sample labeled superpixels according to an image similarity score, which allows us to obtain a balanced set of samples. We then formulate label transfer as an efficient filtering procedure, which lets us exploit more labeled samples than existing techniques. Our experiments evidence the benefits of our approach over state-of-the-art nonparametric methods on two benchmark datasets.
DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes
Authors: Saumitro Dasgupta, Kuan Fang, Kevin Chen, Silvio Savarese
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In this paper, we present a method that uses a fully convolutional neural network (FCNN) in conjunction with a novel optimization framework for generating layout estimates. We demonstrate that our method is robust in the presence of clutter and handles a wide range of highly challenging scenes. We evaluate our method on two standard benchmarks and show that it achieves state of the art results, outperforming previous methods by a wide margin.
A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification
Authors: Siyu Zhu, Richard Zanibbi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character/word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes.
Reversible Recursive Instance-Level Object Segmentation
Authors: Xiaodan Liang, Yunchao Wei, Xiaohui Shen, Zequn Jie, Jiashi Feng, Liang Lin, Shuicheng Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two sub-networks during joint training, in which the refined object proposals and improved segmentation predictions are alternately fed into each other to progressively increase the network capabilities. By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing. Furthermore, to handle multiple overlapped instances within a proposal, an  instance-aware denoising autoencoder is introduced into the segmentation sub-network to distinguish the dominant object from other distracting instances. Extensive experiments on the challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of R2-IOS over other state-of-the-art methods. In particular, the AP^r over 20 classes at 0.5 IoU achieves 66.7%, which significantly outperforms the results of 58.7% by PFN[15] and 46.3% by[17].
Coherent Parametric Contours for Interactive Video Object Segmentation
Authors: Yao Lu, Xue Bai, Linda Shapiro, Jue Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Interactive video segmentation systems aim at producing sub-pixel-level object boundaries for visual effect applications. Recent approaches mainly focus on using sparse user input (i.e. scribbles) for efficient segmentation; however, the quality of the final object boundaries is not satisfactory for the following reasons: (1) the boundary on each frame is often not accurate; (2) boundaries across adjacent frames wiggle around inconsistently, causing temporal flickering; and (3) there is a lack of direct user control for fine tuning.  We propose Coherent Parametric Contours, a novel video segmentation propagation framework that addresses all the above issues. Our approach directly models the object boundary using a set of parametric curves,  providing direct user controls for manual adjustment. A spatio-temporal optimization algorithm is employed to produce object boundaries that are spatially accurate and temporally stable. We show that existing evaluation datasets are limited and demonstrate a new set to cover the common cases in professional rotoscoping. A new metric for evaluating temporal consistency is proposed. Results show that our approach generates higher quality, more coherent segmentation results than previous methods.
Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels
Authors: Yong-Jin Liu, Cheng-Chi Yu, Min-Jing Yu, Ying He
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions (e.g., with high intensity or color variation) and large superpixels in content-sparse regions. Rather than the conventional SLIC method that clusters pixels in R5, we map the image I to a 2-dimensional manifold M in R5, whose area elements are a good measure of the content density in I. We propose an efficient method to compute restricted centroidal Voronoi tessellation (RCVT) --- a uniform tessellation --- on M, which induces the content-sensitive superpixels in I. Unlike other algorithms that characterize content-sensitivity by geodesic distances, manifold SLIC tackles the problem by measuring areas of Voronoi cells on M, which can be computed at a very low cost. As a result, it runs 10 times faster than the state-of-the-art content-sensitive superpixels algorithm. We evaluate manifold SLIC and seven representative methods on the BSDS500 benchmark and observe that our method outperforms the existing methods.
Deep Saliency With Encoded Low Level Distance Map and High Level Features
Authors: Gayoung Lee, Yu-Wing Tai, Junmo Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. They have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that the hand-crafted features can provide complementary effects to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a CNN with multiple 1*1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve performance of the state-of-the-art deep learning based saliency detection methods.
Instance-Level Segmentation for Autonomous Driving With Deep Densely Connected MRFs
Authors: Ziyu Zhang, Sanja Fidler, Raquel Urtasun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [Zhang et al., ICCV15] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image.  A simple Markov random field model using several heuristics was then proposed in [Zhang et al., ICCV15] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field  and show how to encode various intuitive potentials in a way that is amenable to  efficient mean field inference [Krahenbuhl et al., NIPS11]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [Geiger et al., CVPR12] demonstrate that our method achieves a significant performance boost over the baseline [Zhang et al., ICCV15].
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Authors: Nian Liu, Junwei Han
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Traditional1 salient object detection models often use hand-crafted features to formulate contrast and various prior knowledge, and then combine them artificially. In this work, we propose a novel end-to-end deep hierarchical saliency network (DHSNet) based on convolutional neural networks for detecting salient objects. DHSNet first makes a coarse global prediction by automatically learning various global structured saliency cues, including global contrast, objectness, compactness, and their optimal combination. Then a novel hierarchical recurrent convolutional neural network (HRCNN) is adopted to further hierarchically and progressively refine the details of saliency maps step by step via integrating local context information. The whole architecture works in a global to local and coarse to fine manner. DHSNet is directly trained using whole images and corresponding ground truth saliency masks. When testing, saliency maps can be generated by directly and efficiently feedforwarding testing images through the network, without relying on any other techniques. Evaluations on four benchmark datasets and comparisons with other 11 state-of-the-art algorithms demonstrate that DHSNet not only shows its significant superiority in terms of performance, but also achieves a real-time speed of 23 FPS on modern GPUs.
Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking
Authors: Rong Quan, Junwei Han, Dingwen Zhang, Feiping Nie
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, unscalable prior, or low flexibility and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel two-stage co-segmentation framework, which introduces the weak background prior to establish a globally close- loop graph to represent the common object and union background separately. Then a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-segment the common objects. Experiments on three image datasets demonstrate that our method outperforms other state-of-the-art methods.
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions
Authors: Won-Dong Jang, Chulwoo Lee, Chang-Su Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.
Automatic Fence Segmentation in Videos of Dynamic Scenes
Authors: Renjiao Yi, Jue Wang, Ping Tan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present a fully automatic approach to detect and segment fence-like occluders from a video clip. Unlike previous approaches that usually assume either static scenes or cameras, our method is capable of handling both dynamic scenes and moving cameras. Under a bottom-up framework, it first clusters pixels into coherent groups using color and motion features. These pixel groups are then analyzed in a fully connected graph, and labeled as either fence or non-fence using graph-cut optimization. Finally, we solve a dense Conditional Random Filed (CRF) constructed from multiple frames to enhance both spatial accuracy and temporal coherence of the segmentation. Once segmented, one can use existing hole-filling methods to generate a fence-free output. Extensive evaluation suggests that our method outperforms previous automatic and interactive approaches on complex examples captured by mobile devices.
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos
Authors: Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a motion-based method to discover the physical parts of an articulated object class (e.g. head/torso/leg of a horse) from multiple videos. The key is to find object regions that exhibit consistent motion relative to the rest of the object, across multiple videos. We can then learn a location model for the parts and segment them accurately in the individual videos using an energy function that also enforces temporal and spatial consistency in part motion. Unlike our approach, traditional methods for motion segmentation or non-rigid structure from motion operate on one video at a time. Hence they cannot discover a part unless it displays independent motion in that particular video. We evaluate our method on a new dataset of 32 videos of tigers and horses, where we significantly outperform a recent motion segmentation method on the task of part discovery (obtaining roughly twice the accuracy).
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Authors: Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, Alexander Sorkine-Hornung
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Over the years, datasets and benchmarks have proven their fundamental importance in computer vision research, enabling targeted progress and objective comparisons in many fields. At the same time, legacy datasets may impend the evolution of a field due to saturated algorithm performance and the lack of contemporary, high quality data. In this work we present a new benchmark dataset and evaluation methodology for the area of video object segmentation. The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes. Each video is accompanied by densely annotated, pixel-accurate and per-frame ground truth segmentation. In addition, we provide a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics that measure the spatial extent of the segmentation, the accuracy of the silhouette contours and the temporal coherence. The results uncover strengths and weaknesses of current approaches, opening up promising directions for future works.
Learning Temporal Regularity in Video Sequences
Authors: Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, Larry S. Davis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of `meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.
Bilateral Space Video Segmentation
Authors: Nicolas Maerki, Federico Perazzi, Oliver Wang, Alexander Sorkine-Hornung
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatio-temporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering
Authors: Zhang Zhang, Kaiqi Huang, Tieniu Tan, Peipei Yang, Jun Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReD-SFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art.
Training Region-Based Object Detectors With Online Hard Example Mining
Authors: Abhinav Shrivastava, Abhinav Gupta, Ross Girshick
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been -- detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.
Deep Residual Learning for Image Recognition
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.  The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
You Only Look Once: Unified, Real-Time Object Detection
Authors: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.  Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
LocNet: Improving Localization Accuracy for Object Detection
Authors: Spyros Gidaris, Nikos Komodakis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems. Our model, given a search region, aims at returning the bounding box of an object of interest inside this region. To accomplish its goal, it relies on assigning conditional probabilities to each row and column of this region, where these probabilities provide useful information regarding the location of the boundaries of the object inside the search region and allow the accurate inference of the object bounding box under a simple probabilistic framework.   For implementing our localization model, we make use of a convolutional neural network architecture that is properly adapted for this task, called LocNet. We show experimentally that LocNet achieves a very significant improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and that it can be very easily coupled with recent state-of-the-art object detection systems, helping them to boost their performance. Finally, we demonstrate that our detection approach can achieve high detection accuracy even when it is given as input a set of sliding windows, thus proving that it is independent of box proposal methods.