pwc-archive/papers-with-abstracts huggingface dataset:
Event Detection using Multi-Level Relevance Labels and Multiple Features
Authors: Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma, Alexander G. Hauptmann
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Event Detection

We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance.
Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
Authors: Stephan Liwicki, Minh-Tri Pham, Stefanos Zafeiriou, Maja Pantic, Bjorn Stenger
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Shape Recognition, Object Tracking, Translation

In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale and translation which outperforms the previous state-of-the-art approach on a public dataset. For the latter, we incorporate online subspace learning with the proposed FAQ representation to highlight the benefits of the new representation.
Semi-supervised Spectral Clustering for Image Set Classification
Authors: Arif Mahmood, Ajmal Mian, Robyn Owens
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, Clustering, General Classification

We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where  labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm.  In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.
Look at the Driver, Look at the Road: No Distraction! No Accident!
Authors: Mahdi Rezaei, Reinhard klette
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Pose Estimation, Pose Estimation, vehicle detection

The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptation of Global Haar (GHaar) classifiers for vehicle detection under challenging lighting conditions. The system defines the driver's direction of attention (in 6 degrees of freedom), yawning and head-nodding detection, as well as vehicle detection, and distance estimation. Having both road and driver's behaviour information, and implementing a fuzzy fusion system, we develop an integrated framework to cover all of the above subjects. We provide real-time performance analysis for real-world driving scenarios.
Measuring Distance Between Unordered Sets of Different Sizes
Authors: Andrew Gardner, Jinko Kanno, Christian A. Duncan, Rastko Selmic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Information Retrieval, Retrieval

We present a distance metric based upon the notion of minimum-cost injective mappings between sets. Our function satisfies metric properties as long as the cost of the minimum mappings is derived from a semimetric, for which the triangle inequality is not necessarily satisfied. We show that the Jaccard distance (alternatively biotope, Tanimoto, or Marczewski-Steinhaus distance) may be considered the special case for finite sets where costs are derived from the discrete metric. Extensions that allow premetrics (not necessarily symmetric), multisets (generalized to include probability distributions), and asymmetric mappings are given that expand the versatility of the metric without sacrificing metric properties. The function has potential applications in pattern recognition, machine learning, and information retrieval.
Learning Mid-level Filters for Person Re-identification
Authors: Rui Zhao, Wanli Ouyang, Xiaogang Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, Patch Matching, Person Re-Identification

In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
Authors: Wei Li, Rui Zhao, Tong Xiao, Xiaogang Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Person Re-Identification

Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection.  In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. The learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.
Lacunarity Analysis on Image Patterns for Texture Classification
Authors: Yuhui Quan, Yong Xu, Yuping Sun, Yu Luo
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Texture Classification

Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes. The texture feature is constructed by concatenating the lacunarity-related parameters estimated from the multi-scale local binary patterns of image. Benefiting from the ability of lacunarity analysis to distinguish spatial patterns, our method is able to characterize the spatial distribution of local image structures from multiple scales. The proposed feature was applied to texture classification and has demonstrated excellent performance in comparison with several state-of-the-art approaches on four benchmark datasets.
Segmentation-aware Deformable Part Models
Authors: Eduard Trulls, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Optical Flow Estimation, Segmentation, Superpixels

In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).  The merit of our approach lies in "cleaning up" the low-level HOG features by exploiting the spatial support of SLIC superpixels; this can be understood as using segmentation to split the feature variation into object-specific  and background changes. Rather than committing to a single segmentation we use a large pool of SLIC superpixels and combine them in a scale-, position- and object-dependent manner to build soft segmentation masks. The segmentation masks can be computed fast enough to repeat this process over every candidate window, during training and detection, for both the root and part filters of DPMs.  We use these masks to construct enhanced, background-invariant features to train DPMs. We test our approach on the PASCAL VOC 2007, outperforming the standard DPM in 17 out of 20 classes, yielding an average increase of 1.7% AP. Additionally, we demonstrate the robustness of this approach, extending it to dense SIFT descriptors for large displacement optical flow.
From Categories to Individuals in Real Time -- A Unified Boosting Approach
Authors: David Hall, Pietro Perona
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object

A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking.  Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets.
NMF-KNN: Image Annotation using Weighted Multi-view Non-negative Matrix Factorization
Authors: Mahdi M. Kalayeh, Haroon Idrees, Mubarak Shah
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: TAG

The real world image databases such as Flickr are characterized by continuous addition of new images. The recent approaches for image annotation, i.e. the problem of assigning tags to images, have two major drawbacks. First, either models are learned using the entire training data, or to handle the issue of dataset imbalance, tag-specific discriminative models are trained. Such models become obsolete and require relearning when new images and tags are added to database. Second, the task of feature-fusion is typically dealt using ad-hoc approaches. In this paper, we present a weighted extension of Multi-view Non-negative Matrix Factorization (NMF) to address the aforementioned drawbacks. The key idea is to learn query-specific generative model on the features of nearest-neighbors and tags using the proposed NMF-KNN approach which imposes consensus constraint on the coefficient matrices across different features. This results in coefficient vectors across features to be consistent and, thus, naturally solves the problem of feature fusion, while the weight matrices introduced in the proposed formulation alleviate the issue of dataset imbalance. Furthermore, our approach, being query-specific, is unaffected by addition of images and tags in a database. We tested our method on two datasets used for evaluation of image annotation and obtained competitive results.
Fine-Grained Visual Comparisons with Local Learning
Authors: Aron Yu, Kristen Grauman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute

Given two images, we want to predict which exhibits a particular visual attribute more than the other---even when the two images are quite similar.  Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering.  To address these issues, we propose a local learning approach for fine-grained visual comparisons.  Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons.  We show how to identify these analogous pairs using learned metrics.  With results on three challenging datasets -- including a large newly curated dataset for fine-grained comparisons -- our method outperforms state-of-the-art methods for relative attribute prediction.
Inferring Analogous Attributes
Authors: Chao-Yeh Chen, Kristen Grauman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Transfer Learning

The appearance of an attribute can vary considerably from class to class (e.g., a "fluffy" dog vs. a "fluffy" towel), making standard class-independent attribute models break down. Yet, training object-specific models for each attribute can be impractical, and defeats the purpose of using attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can infer new ones for object-attribute pairs unobserved during training. For example, even though the system has no labeled images of striped dogs, it can use its knowledge of other attributes and objects to tailor "stripedness" to the dog category. With two large-scale datasets, we demonstrate both the need for category-sensitive attributes as well as our method's successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer.
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes
Authors: Lucy Liang, Kristen Grauman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Active Learning, Attribute, Diversity

It is useful to automatically compare images based on their visual properties---to predict which image is brighter, more feminine, more blurry, etc.  However, comparative models are inherently more costly to train than their classification counterparts.  Manually labeling all pairwise comparisons is intractable, so which pairs should a human supervisor compare?  We explore active learning strategies for training relative attribute ranking functions, with the goal of requesting human comparisons only where they are most informative.  We introduce a novel criterion that requests a partial ordering for a set of examples that minimizes the total rank margin in attribute space, subject to a visual diversity constraint.  The setwise criterion helps amortize effort by identifying mutually informative comparisons, and the diversity requirement safeguards against requests a human viewer will find ambiguous.  We develop an efficient strategy to search for sets that meet this criterion.  On three challenging datasets and experiments with "live" online annotators, the proposed method outperforms both traditional passive learning as well as existing active rank learning methods.
Visual Persuasion: Inferring Communicative Intents of Images
Authors: Jungseock Joo, Weixin Li, Francis F. Steen, Song-Chun Zhu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as "socially dominant," "energetic," and "trustworthy," and propose a hierarchical model that builds on the layer of syntactical attributes, such as "smile" and "waving hand," to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1,124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of investigations around mediated images, intersecting with media analysis, psychology, and political communication.
Histograms of Pattern Sets for Image Classification and Object Recognition
Authors: Winn Voravuthikunchai, Bruno Cremilleux, Frederic Jurie
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Binarization, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection, Object Recognition

This paper introduces a novel image representation capturing feature  dependencies through the mining of meaningful combinations of visual   features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of  projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.
Incorporating Scene Context and Object Layout into Appearance Modeling
Authors: Hamid Izadinia, Fereshteh Sadeghi, Ali Farhadi
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Scene Understanding

A scene category imposes tight distributions over the kind of objects that might appear in the scene, the appearance of those objects and their layout. In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout. Our experimental evaluations show that our learned scene structures outperform state-of-the-art method of Deformable Part Models in detecting objects in a scene. Our scene structure provides a level of scene understanding that is amenable to deep visual inferences. The scene structures can also generate features that can later be used for scene categorization. Using these features, we also show promising results on scene categorization.
Co-Segmentation of Textured 3D Shapes with Sparse Annotations
Authors: Mehmet Ersin Yumer, Won Chun, Ameesh Makadia
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Segmentation

We present a novel co-segmentation method for textured 3D shapes. Our algorithm takes a collection of textured shapes belonging to the same category and sparse annotations of foreground segments, and produces a joint dense segmentation of the shapes in the collection. We model the segments by a collectively trained Gaussian mixture model. The final model segmentation is formulated as an energy minimization across all models jointly, where intra-model edges control the smoothness and separation of model segments, and inter-model edges impart global consistency. We show promising results on two large real-world datasets, and also compare with previous shape-only 3D segmentation methods using publicly available datasets.
How to Evaluate Foreground Maps?
Authors: Ran Margolin, Lihi Zelnik-Manor, Ayellet Tal
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Segmentation, Semantic Segmentation

The output of many algorithms in computer-vision is either non-binary maps or binary maps (e.g., salient object detection and object segmentation). Several measures have been suggested to evaluate the accuracy of these foreground maps. In this paper, we show that the most commonly-used measures for evaluating both non-binary maps and binary maps do not always provide a reliable evaluation. This includes the Area-Under-the-Curve measure,  the Average-Precision measure, the F-measure, and the evaluation measure of the PASCAL VOC segmentation challenge. We start by identifying three causes of inaccurate evaluation. We then propose a new measure that amends these flaws. An appealing property of our measure is being an intuitive generalization of the F-measure. Finally we propose four meta-measures to compare the adequacy of evaluation measures. We show via experiments that our novel measure is preferable.