pwc-archive/papers-with-abstracts huggingface dataset:
SOLD: Sub-Optimal Low-rank Decomposition for Efficient Video Segmentation
Authors: Chenglong Li, Liang Lin, WangMeng Zuo, Shuicheng Yan, Jin Tang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Video Segmentation, Video Semantic Segmentation

This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions. We propose a general algorithm, called Sub-Optimal Low-rank Decomposition (SOLD), which pursues the low-rank representation for video segmentation. Given the supervoxels affinity matrix of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the affinity matrix with the rank fixed can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into our framework based on the obervation that small-size video patterns tend to recur frequently within the same object. The video can be segmented into several spatio-temporal regions by applying the Normalized-Cut (NCut) algorithm with the solved low-rank representation. To process the streaming videos, we apply our algorithm sequentially over a batch of frames over time, in which we also develop several temporal consistent constraints improving the robustness. Extensive experiments on the public benchmarks demonstrate superior performance of our framework over other state-of-the-art approaches.
On the Appearance of Translucent Edges
Authors: Ioannis Gkioulekas, Bruce Walter, Edward H. Adelson, Kavita Bala, Todd Zickler
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Edges in images of translucent objects are very different from edges in images of opaque objects. The physical causes for these differences are hard to characterize analytically and are not well understood. This paper considers one class of translucency edges---those caused by a discontinuity in surface orientation---and describes the physical causes of their appearance. We simulate thousands of translucency edge profiles using many different scattering material parameters, and we explain the resulting variety of edge patterns by qualitatively analyzing light transport. We also discuss the existence of shape and material metamers, or combinations of distinct shape or material parameters that generate the same edge profile. This knowledge is relevant to visual inference tasks that involve translucent objects, such as shape or material estimation.
Fine-Grained Recognition Without Part Annotations
Authors: Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: All

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.
Robust Reconstruction of Indoor Scenes
Authors: Sungjoon Choi, Qian-Yi Zhou, Vladlen Koltun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: global-optimization, Indoor Scene Reconstruction

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models.
One-Shot Learning of Scene Locations via Feature Trajectory Transfer
Authors: Roland Kwitt, Sebastian Hegenbart, Marc Niethammer
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, One-Shot Learning, Scene Recognition

The appearance of (outdoor) scenes changes considerably with the strength of certain transient attributes, such as "rainy", "dark" or "sunny". Obviously, this also affects the representation of an image in feature space, e.g., as activations at a certain CNN layer, and consequently impacts scene recognition performance. In this work, we investigate the variability in these transient attributes as a rich source of information for studying how image representations change as a function of attribute strength. In particular, we leverage a recently introduced dataset with fine-grain annotations to estimate feature trajectories for a collection of transient attributes and then show how these trajectories can be transferred to new image representations. This enables us to synthesize new data along the transferred trajectories with respect to the dimensions of the space spanned by the transient attributes. Applicability of this concept is demonstrated on the problem of one-shot recognition of scene locations. We show that data synthesized via feature trajectory transfer considerably boosts recognition performance, (1) with respect to baselines and (2) in combination with state-of-the-art approaches in one-shot learning.
The Global Patch Collider
Authors: Shenlong Wang, Sean Ryan Fanello, Christoph Rhemann, Shahram Izadi, Pushmeet Kohli
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

This paper proposes a novel extremely efficient, fully-parallelizable, task-specific algorithm for the computation of global point-wise correspondences in images and videos. Our algorithm, the Global Patch Collider, is based on detecting unique collisions between image points using a collection of learned tree structures that act as conditional hash functions. In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures. The split functions stored at the intermediate nodes of the trees are trained to ensure that only visually similar patches or their geometric or photometric transformed versions fall into the same leaf node. The matching process involves passing all pixel positions in the images under analysis through the tree structures. We then compute matches by isolating points that uniquely collide with each other  ie. fell in the same empty leaf in multiple trees. Our algorithm is linear in the number of pixels but can be made constant time on a parallel computation architecture as the tree traversal for individual image points is decoupled. We demonstrate the efficacy of our method by using it to perform optical flow matching and stereo matching on some challenging benchmarks. Experimental results show that not only is our method extremely computationally efficient, but it is also able to match or outperform state of the art methods that are much more complex.
Joint Probabilistic Matching Using m-Best Solutions
Authors: Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems can be improved by considering multiple hypotheses before computing the final similarity measure. To that end, we propose to utilize the marginal distributions for each entity. Previously, this idea has been neglected mainly because exact marginalization is intractable due to a combinatorial number of all possible matching permutations. Here, we propose a generic approach to efficiently approximate the marginal distributions by exploiting the m-best solutions of the original problem. This approach not only improves the matching solution, but also provides more accurate ranking of the results, because of the extra information included in the marginal distribution. We validate our claim on two distinct objectives: (i) person re-identification and temporal matching modelled as an integer linear program, and (ii) feature point matching using a quadratic cost function. Our experiments confirm that marginalization indeed leads to superior performance compared to the single (nearly) optimal solution, yielding state-of-the-art results in both applications on standard benchmarks.
Interactive Segmentation on RGBD Images via Cue Selection
Authors: Jie Feng, Brian Price, Scott Cohen, Shih-Fu Chang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Retrieval, Image Segmentation, Interactive Segmentation, Object Recognition, Retrieval, Segmentation, Semantic Segmentation

Interactive image segmentation is an important problem in computer vision with many applications including image editing, object recognition and image retrieval. Most existing interactive segmentation methods only operate on color images. Until recently, very few works have been proposed to leverage depth information from low-cost sensors to improve interactive segmentation. While these methods achieve better results than color-based methods, they are still limited in either using depth as an additional color channel or simply combining depth with color in a linear way. We propose a novel interactive segmentation algorithm which can incorporate multiple feature cues like color, depth, and normals in an unified graph cut framework to leverage these cues more effectively. A key contribution of our method is that it automatically selects a single cue to be used at each pixel, based on the intuition that only one cue is necessary to determine the segmentation label locally.  This is achieved by optimizing over both segmentation labels and cue labels, using terms designed to decide where both the segmentation and label cues should change. Our algorithm thus produces not only the segmentation mask but also a cue label map that indicates where each cue contributes to the final result. Extensive experiments on five large scale RGBD datasets show that our proposed algorithm performs significantly better than both other color-based and RGBD based algorithms in reducing the amount of user inputs as well as increasing segmentation accuracy.
Layered Scene Decomposition via the Occlusion-CRF
Authors: Chen Liu, Pushmeet Kohli, Yasutaka Furukawa
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Semantic Segmentation

This paper addresses the challenging problem of perceiving the hidden or occluded geometry of the scene depicted in any given RGBD image. Unlike other image labeling problems such as image segmentation where each pixel needs to be assigned a single label, layered decomposition requires us to assign multiple labels to pixels. We propose a novel "Occlusion-CRF" model that allows for the integration of sophisticated priors to regularize the solution space and enables the automatic inference of the layer decomposition. We use a generalization of the Fusion Move algorithm to perform Maximum a Posterior (MAP) inference on the model that can handle the large label sets needed to represent multiple surface assignments to each pixel. We have evaluated the proposed model and the inference algorithm on many RGBD images of cluttered indoor scenes. Our experiments show that not only is our model able to explain occlusions but it also enables automatic inpainting of occluded/invisible surfaces.
Learning Relaxed Deep Supervision for Better Edge Detection
Authors: Yu Liu, Michael S. Lew
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Edge Detection

We propose using relaxed deep supervision (RDS) within convolutional neural networks for edge detection. The conventional deep supervision utilizes the general ground-truth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional relaxed labels to consider the diversities in deep neural networks. We begin by capturing the relaxed labels from simple detectors (e.g. Canny).Then we merge them with the general ground-truth to generate the RDS. Finally we employ the RDS to supervise the edge network following a coarse-to-fine paradigm. These relaxed labels can be seen as some false positives that are difficult to be classified. We consider these false positives in the supervision, and are able to achieve high performance for better edge detection. We compensate for the lack of training images by capturing coarse edge annotations from a large dataset of image segmentations to pretrain the model. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the well-known BSDS500 dataset (ODS F-score of .792) and obtains superior cross-dataset generalization results on NYUD dataset.
Occlusion Boundary Detection via Deep Exploration of Context
Authors: Huan Fu, Chaohui Wang, DaCheng Tao, Michael J. Black
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Boundary Detection, Object Recognition, Scene Understanding

Occlusion boundaries contain rich perceptual information about the underlying scene structure. They also provide important cues in many visual perception tasks such as scene understanding, object recognition, and segmentation. In this paper, we improve occlusion boundary detection via enhanced exploration of contextual information (e.g., local structural boundary patterns, observations from surrounding regions, and temporal context), and in doing so develop a novel approach based on convolutional neural networks (CNNs) and conditional random fields (CRFs). Experimental results demonstrate that our detector significantly outperforms the state-of-the-art (e.g., improving the F-measure from 0.62 to 0.71 on the commonly used CMU benchmark). Last but not least, we empirically assess the roles of several important components of the proposed detector, so as to validate the rationale behind this approach.
Learning to Localize Little Landmarks
Authors: Saurabh Singh, Derek Hoiem, David Forsyth
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

We interact everyday with tiny objects such as the door handle of a car or the light switch in a room. These little landmarks are barely visible and hard to localize in images. We describe a method to find such landmarks by finding a sequence of latent landmarks, each with a prediction model. Each latent landmark predicts the next in sequence, and the last localizes the target landmark. For example, to find the door handle of a car, our method learns to start with a latent landmark near the wheel, as it is globally distinctive; subsequent latent landmarks use the context from the earlier ones to get closer to the target. Our method is supervised solely by the location of the little landmark and displays strong performance on more difficult variants of established tasks and on two new tasks.
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction
Authors: Edgar Simo-Serra, Hiroshi Ishikawa
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification

We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extraction network with a ranking loss and a classification network with a cross-entropy loss. We obtain high-quality compact discriminative features with few parameters, learned on relatively small datasets without additional annotations. This enables us to tackle tasks with specialized images not very similar to the more generic ones in existing fully-supervised datasets. We show that the resulting features in combination with a linear classifier surpass the state-of-the-art on the Hipster Wars dataset despite using features only 0.3% of the size. Our proposed features significantly outperform those obtained from networks trained on ImageNet, despite being 32 times smaller (128 single-precision floats), trained on noisy and weakly-labeled data, and using only 1.5% of the number of parameters.
Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis
Authors: Yuhui Quan, Chenglong Bao, Hui Ji
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Computational Efficiency, Dictionary Learning, Texture Classification

Most existing dictionary learning algorithms consider a linear sparse model, which often  cannot effectively characterize the nonlinear properties present in many types of visual data, e.g. dynamic texture (DT). Such nonlinear properties can be exploited by the so-called  kernel sparse coding. This paper proposed an equiangular kernel dictionary learning method with optimal mutual coherence to exploit  the nonlinear sparsity of  high-dimensional visual data. Two main issues are addressed in the proposed method: (1) coding stability for redundant dictionary of infinite-dimensional space; and (2) computational efficiency for computing kernel matrix of training samples of high-dimensional data. The proposed kernel sparse coding method is applied to dynamic texture analysis with both local DT pattern extraction and global DT pattern characterization. The experimental results showed its performance gain over existing methods.
Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales
Authors: Tsun-Yi Yang, Yen-Yu Lin, Yung-Yu Chuang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

This paper proposes a novel local descriptor through accumulated stability voting (ASV). The stability of feature dimensions is measured by their differences across scales. To be more robust to noise, the stability is further quantized by thresholding. The principle of maximum entropy is utilized for determining the best thresholds for maximizing discriminant power of the resultant descriptor. Accumulating stability renders a real-valued descriptor and it can be converted into a binary descriptor by an additional thresholding process. The real-valued descriptor attains high matching accuracy while the binary descriptor makes a good compromise between storage and accuracy. Our descriptors are simple yet effective, and easy to implement. In addition, our descriptors require no training. Experiments on popular benchmarks demonstrate the effectiveness of our descriptors and their superiority to the state-of-the-art descriptors.
CoMaL: Good Features to Match on Object Boundaries
Authors: Swarna K. Ravindran, Anurag Mittal
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object

Traditional Feature Detectors and Trackers use information aggregation in 2D patches to detect and match discriminative patches.  However, this information does not remain the same at object boundaries when there is object motion against a significantly varying background.  In this paper, we propose a new approach for feature detection, tracking and re-detection that gives significantly improved results at the object boundaries.  We utilize level lines or iso-intensity curves that often remain stable and can be reliably detected even at the object boundaries, which they often trace. Stable portions of long level lines are detected and points of high curvature  are detected on such curves for corner detection.  Further, this level line is used to separate the portions belonging to the two objects, which is then used for robust matching of such points.  While such CoMaL (Corners on Maximally-stable Level Line Segments) points were found to be much more reliable at the object boundary regions, they perform comparably at the interior regions as well. This is illustrated in exhaustive experiments on real-world datasets.
Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment
Authors: Yuan-Ting Hu, Yen-Yu Lin
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

We address two difficulties in establishing an accurate system for image matching. First, image matching relies on the descriptor for feature extraction, but the optimal descriptor often varies from image to image, or even patch to patch. Second, conventional matching approaches carry out geometric checking on a small set of correspondence candidates due to the concern of efficiency. It may result in restricted performance in recall. We aim at tackling the two issues by integrating adaptive descriptor selection and progressive candidate enrichment into image matching. We consider that the two integrated components are complementary: The high-quality matching yielded by adaptively selected descriptors helps in exploring more plausible candidates, while the enriched candidate set serves as a better reference for descriptor selection. It motivates us to formulate image matching as a joint optimization problem, in which adaptive descriptor selection and progressive correspondence enrichment are alternately conducted. Our approach is comprehensively evaluated and compared with the state-of-the-art approaches on two benchmarks. The promising results manifest its effectiveness.
A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection
Authors: Da Chen, Jean-Marie Mirebeau, Laurent D. Cohen
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Contour Detection, Image Segmentation, Semantic Segmentation

In this paper, we propose a new curvature penalized minimal path model for image segmentation via closed contour detection based on the weighted Euler elastica curves, firstly introduced to the field of computer vision in [22]. Our image segmentation method extracts a collection of curvature penalized minimal geodesics, concatenated to form a closed contour, by connecting a set of user-specified points. Globally optimal minimal paths can be computed by solving an Eikonal equation. This first order PDE is traditionally regarded as unable to penalize curvature, which is related to the path acceleration in active contour models. We introduce here a new approach that enables finding a global minimum of the geodesic energy including a curvature term. We achieve this through the use of a novel Finsler metric adding to the image domain the orientation as an extra space dimension. This metric is non-Riemannian and asymmetric, defined on an orientation lifted space, incorporating the curvature penalty in the geodesic energy. Experiments show that the proposed Finsler minimal path model indeed outperforms state-of-the-art minimal path models in both synthetic and real images.