pwc-archive/papers-with-abstracts huggingface dataset:
Predicting User Annoyance Using Visual Attributes
Authors: Gordon Christie, Amar Parkash, Ujwal Krothapalli, Devi Parikh
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Image Retrieval, Retrieval

Computer Vision algorithms make mistakes. In human-centric applications, some mistakes are more annoying to users than others. In order to design algorithms that minimize the annoyance to users, we need access to an annoyance or cost matrix that holds the annoyance of each type of mistake. Such matrices are not readily available, especially for a wide gamut of human-centric applications where annoyance is tied closely to human perception. To avoid having to conduct extensive user studies to gather the annoyance matrix for all possible mistakes, we propose predicting the annoyance of previously unseen mistakes by learning from example mistakes and their corresponding annoyance. We promote the use of attribute-based representations to transfer this knowledge of annoyance. Our experimental results with faces and scenes demonstrate that our approach can predict annoyance more accurately than baselines. We show that as a result, our approach makes less annoying mistakes in a real-world image retrieval application.
Linear Ranking Analysis
Authors: Weihong Deng, Jiani Hu, Jun Guo
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Zero-Shot Learning

We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace. Under the constrain on the ranking order of the classes, two criteria are proposed: 1) minimization of the classification error with the assumption that each class is homogenous Guassian distributed; 2) maximization of the sum (average) of the K minimum distances of all neighboring-class (centroid) pairs. Both criteria can be efficiently solved by the convex optimization for one-dimensional subspace. Greedy algorithm is applied to extend the results to the multi-dimensional subspace. Experimental results show that 1) LRA with both criteria achieve state-of-the-art performance on the tasks of ranking learning and zero-shot learning; and 2) the maximum margin criterion provides a discriminative subspace selection method, which can significantly remedy the class separation problem in comparing with several representative extensions of LDA.
Transformation Pursuit for Image Classification
Authors: Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, image-classification, Image Classification

A simple approach to learning invariances in image clas- sification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a com- pact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transfor- mations increases training time with no gain in accuracy. We propose a principled algorithm  Image Transformation Pursuit (ITP)  for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by se- lecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an im- provement from 70.1% to 74.9% in top-5 accuracy on Im- ageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.
Incremental Learning of NCM Forests for Large-Scale Image Classification
Authors: Marko Ristin, Matthieu Guillaumin, Juergen Gall, Luc van Gool
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, image-classification, Image Classification, Incremental Learning, Small Data Image Classification

In recent years, large image data sets such as "ImageNet", "TinyImages" or ever-growing social networks like "Flickr" have emerged, posing new challenges to image classification that were not apparent in smaller image sets. In particular, the efficient handling of dynamically growing data sets, where not only the amount of training images, but also the number of classes increases over time, is a relatively unexplored problem. To remedy this, we introduce Nearest Class Mean Forests (NCMF), a variant of Random Forests where the decision nodes are based on nearest class mean (NCM) classification. NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes. To this end, we propose and compare several approaches to incorporate data from new classes, so as to seamlessly extend the previously trained forest instead of re-training them from scratch. In our experiments, we show that NCMFs trained on small data sets with 10 classes can be extended to large data sets with 1000 classes without significant loss of accuracy compared to training from scratch on the full data.
Object Classification with Adaptable Regions
Authors: Hakan Bilen, Marco Pedersoli, Vinay P. Namboodiri, Tinne Tuytelaars, Luc van Gool
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Object

In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels. Unlike these works, in this paper, we propose to enhance the semantic representation of an image. We aim to learn the most important visual components of an image and how they interact in order to classify the objects correctly. To achieve our objective, we propose a new latent SVM model for category level object classification. Starting from image-level annotations, we jointly learn the object class and its context in terms of spatial location (where) and appearance (what). Furthermore, to regularize the complexity of the model we learn the spatial and  co-occurrence relations between adjacent regions, such that unlikely configurations are penalized. Experimental results demonstrate that the proposed method can consistently enhance results on the challenging Pascal VOC dataset in terms of classification and weakly supervised detection. We also show how semantic representation can be exploited for finding similar content.
Discriminative Ferns Ensemble for Hand Pose Recognition
Authors: Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Object Recognition

We present the Discriminative Ferns Ensemble (DFE) classifier for efficient visual object recognition. The classifier architecture is designed to optimize both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity model and careful discriminative optimization. The proposed framework is applied to the problem of hand pose recognition in depth and infra-red images, using a very large training set. Both the accuracy and the classification time obtained are considerably superior to relevant competing methods, allowing one to reach accuracy targets with run times orders of magnitude faster than the competition. We show empirically that using DFE, we can significantly reduce classification time by increasing training sample size for a fixed target accuracy. Finally a DFE result is shown for the MNIST dataset,  showing the method's merit extends beyond depth images.
Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects
Authors: Muhammad Zeeshan Zia, Michael Stark, Konrad Schindler
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D geometry, 3D Shape Modeling, Scene Understanding

Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes. While these representations have proven robust in a variety of applications, they provide only coarse approximations to the true 2D and 3D extent of objects. As a result, object-object interactions, such as occlusions or ground-plane contact, can be represented only superficially. In this paper, we approach the problem of scene understanding from the perspective of 3D shape modeling, and design a 3D scene representation that reasons jointly about the 3D shape of multiple objects. This representation allows to express 3D geometry and occlusion on the fine detail level of individual vertices of 3D wireframe models, and makes it possible to treat dependencies between objects, such as occlusion reasoning, in a deterministic way. In our experiments, we demonstrate the benefit of jointly estimating the 3D shape of multiple objects in a scene over working with coarse boxes, on the recently proposed KITTI dataset of realistic street scenes.
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Authors: Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, Bernt Schiele
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 2D Human Pose Estimation, Art Analysis, Diversity, Pose Estimation

Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.
Using a Deformation Field Model for Localizing Faces and Facial Points under Weak Supervision
Authors: Marco Pedersoli, Tinne Tuytelaars, Luc van Gool
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Detection

Face detection and facial points localization are interconnected tasks. Recently it has been shown that solving these two tasks jointly with a mixture of trees of parts (MTP) leads to state-of-the-art results. However, MTP, as most other methods for facial point localization proposed so far, requires a complete annotation of the training data at facial point level. This is used to predefine the structure of the trees and to place the parts correctly. In this work we extend the mixtures from trees to more general loopy graphs. In this way we can learn in a weakly supervised manner (using only the face location and orientation) a powerful deformable detector that implicitly aligns its parts to the detected face in the image. By attaching some reference points to the correct parts of our detector we can then localize the facial points. In terms of detection our method clearly outperforms the state-of-the-art, even if competing with methods that use facial point annotations during training. Additionally, without any facial point annotation at the level of individual training images, our method can localize facial points with an accuracy similar to fully supervised approaches.
Active Annotation Translation
Authors: Steve Branson, Kristjan Eldjarn Hjorleifsson, Pietro Perona
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Translation

We introduce a general framework for quickly annotating an image dataset when previous annotations exist. The new annotations (e.g. part locations) may be quite different from the old annotations (e.g. segmentations).  Human annotators may be thought of as helping translate the old annotations into the new ones.  As annotators label images, our algorithm incrementally learns a translator from source to target labels as well as a computer-vision-based structured predictor. These two components are combined to form an improved prediction system which accelerates the annotators' work through a smart GUI.   We show how the method can be applied to translate between a wide variety of annotation types, including bounding boxes, segmentations, 2D and 3D part-based systems, and class and attribute labels. The proposed system will be a useful tool toward exploring new types of representations beyond simple bounding boxes, object segmentations, and class labels, and toward finding new ways to exploit existing large datasets with traditional types of annotations like SUN, Image Net, and Pascal VOC.  Experiments on the CUB-200-2011 and H3D datasets demonstrate 1) our method accelerates collection of part annotations by a factor of 3-20 compared to manual labeling, 2) our system can be used effectively in a scheme where definitions of part, attribute, or action vocabularies are evolved interactively without relabeling the entire dataset, and 3) toward collecting pose annotations, segmentations are more useful than bounding boxes, and part-level annotations are more effective than segmentations.
Looking Beyond the Visible Scene
Authors: Aditya Khosla, Byoungkwon An An, Joseph J. Lim, Antonio Torralba
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Scene Understanding

A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonalds or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.
Two-Class Weather Classification
Authors: Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Vocal Bursts Valence Prediction

Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. Never adequately addressed, this twoclass classification problem is by no means trivial given the great variety of outdoor images. Our weather feature combines special cues after properly encoding them into feature vectors. They then work collaboratively in synergy under a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. Extensive experiments and comparisons are performed to verify our method. We build a new weather image dataset consisting of 10K sunny and cloudy images, which is available online together with the executable.
Learning Important Spatial Pooling Regions for Scene Classification
Authors: Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Scene Classification

We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. This problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. The experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.
Orientational Pyramid Matching for Recognizing Indoor Scenes
Authors: Lingxi Xie, Jingdong Wang, Baining Guo, Bo Zhang, Qi Tian
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Form, General Classification, Scene Classification, Scene Recognition

Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context modeling. In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling. Our approach is motivated by the observation that the 3D orientations of objects are a crucial factor to discriminate indoor scenes. The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid. Experimental results on challenging scene classification tasks show that OPM achieves the performance comparable with SPM and that OPM and SPM make complementary contributions so that their combination gives the state-of-the-art performance.
Multilabel Ranking with Inconsistent Rankers
Authors: Xin Geng, Longrun Luo
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance. The key idea is to learn a latent preference distribution for each instance. The proposed method mainly includes two steps. The first step is to generate a common preference distribution that is most compatible to all the personal rankings. The second step is to learn a mapping from the instances to the preference distributions. The proposed preference distribution learning (PDL) method is applied to the problem of multilabel ranking for natural scene images. Experimental results show that PDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms.
Scene Parsing with Object Instances and Occlusion Ordering
Authors: Joseph Tighe, Marc Niethammer, Svetlana Lazebnik
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Scene Parsing, valid

 This work proposes a method to interpret a scene by assigning a semantic label at every pixel  and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image,  we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering.  This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to refine the pixel labels and vice versa. The proposed system obtains promising results on two challenging subsets of the LabelMe and SUN datasets,  the largest of which contains 45,676 images and 232 classes.
A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform
Authors: Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C. Vemuri
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: set matching

In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation. This is achieved by solving a static Schrodinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit L_2 norm---making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm.  We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques.  Finally, to evaluate the performance of our algorithm---dubbed SDTM---we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques.  The experiments show that our algorithm outperforms state-of the-art point set registration algorithms on many quantitative metrics.
Seeing 3D Chairs: Exemplar Part-based 2D-3D Alignment using a Large Dataset of CAD Models
Authors: Mathieu Aubry, Daniel Maturana, Alexei A. Efros, Bryan C. Russell, Josef Sivic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the "chair" class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as well as the large variation in viewpoint. We develop an approach to establish part-based correspondences between 3D CAD models and real photographs. This is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints. We demonstrate the ability of our system to align 3D models with 2D objects in the challenging PASCAL VOC images, which depict a wide variety of chairs in complex scenes.