pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Domain Generalization for Visual Recognition
Authors: Li Niu, Wen Li, Dong Xu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Domain Adaptation, Domain Generalization

In this paper, we propose a new multi-view domain generalization (MVDG) approach for visual recognition, in which we aim to use the source domain samples with multiple types of features (i.e., multi-view features) to learn robust classifiers that can generalize well to any unseen target domain. Considering the recent works show the domain generalization capability can be enhanced by fusing multiple SVM classifiers, we build upon exemplar SVMs to learn a set of SVM classifiers by using one positive sample and all negative samples in the source domain each time. When the source domain samples come from multiple latent domains, we expect the weight vectors of exemplar SVM classifiers can be organized into multiple hidden clusters. To exploit such cluster structure, we organize the weight vectors learnt on each view as a weight matrix and seek the low-rank representation by reconstructing this weight matrix using itself as the dictionary. To enforce the consistency of inherent cluster structures discovered from the weight matrices learnt on different views, we introduce a new regularizer to minimize the mismatch between any two representation matrices on different views. We also develop an efficient alternating optimization algorithm and further extend our MVDG approach for domain adaptation by exploiting the manifold structure of unlabeled target domain samples. Comprehensive experiments for visual recognition clearly demonstrate the effectiveness of our approaches for domain generalization and domain adaptation.
Infinite Feature Selection
Authors: Giorgio Roffo, Simone Melzi, Marco Cristani
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Classification, feature selection, General Classification, Object Recognition

Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an infinite number permits the investigation of the importance (relevance and redundancy) of a feature when injected into an arbitrary set of cues. Ranking the importance individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. The Inf-FS has been tested on thirteen diverse benchmarks, comparing against filters, embedded methods, and wrappers; in all the cases we achieve top performances, notably on the classification tasks of PASCAL VOC 2007-2012.
Semi-Supervised Zero-Shot Classification With Label Representation Learning
Authors: Xin Li, Yuhong Guo, Dale Schuurmans
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Classification, General Classification, Multi-class Classification, Prediction, Representation Learning, zero-shot-classification, Zero-Shot Learning

Given the challenge of gathering labeled training data, zero-shot classification, which transfers information from observed classes to recognize unseen classes, has become increasingly popular in the computer vision community. Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem. In this paper, we propose a novel zero-shot classification approach that automatically learns label embeddings from the input data in a semi-supervised large-margin learning framework. The proposed framework jointly considers multi-class classification over all classes (observed and unseen) and tackles the target prediction problem directly without introducing intermediate prediction problems. It also has the capacity to incorporate semantic label information from different sources when available. To evaluate the proposed approach, we conduct experiments on standard zero-shot data sets. The empirical results show the proposed approach outperforms existing state-of-the-art zero-shot learning methods.
A Supervised Low-Rank Method for Learning Invariant Subspaces
Authors: Farzad Siyahjani, Ranya Almohsen, Sinan Sabri, Gianfranco Doretto
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Recognition, Metric Learning, Robust classification

Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems. They build a generative representation of the data, which often requires complex training as well as testing to be robust against data variations induced by nuisance factors. We introduce the invariant components, a discriminative representation invariant to nuisance factors, because it spans subspaces orthogonal to the space where nuisance factors are defined. This allows developing a framework based on geometry that ensures a uniform inter-class separation, and a very efficient and robust classification based on simple nearest neighbor. In addition, we show how the approach is equivalent to a local metric learning, where the local metrics (one for each class) are learned jointly, rather than independently, thus avoiding the risk of overfitting without the need for additional regularization. We evaluated the approach for face recognition with highly corrupted training and testing data, obtaining very promising results.
Recursive Frechet Mean Computation on the Grassmannian and its Applications to Computer Vision
Authors: Rudrasis Chakraborty, Baba C. Vemuri
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Face Recognition, Temporal Action Localization

In the past decade, Grassmann manifolds (Grassmannian) have been commonly used in mathematical formulations of many Computer Vision tasks. Averaging points on a Grassmann manifold is a very common operation in many applications including but not limited to, tracking, action recognition, video-face recognition, face recognition, etc. Computing the intrinsic/Frechet mean (FM) of a set of points on the Grassmann can be cast as finding the global optimum (if it exists) of the sum of squared geodesic distances cost function. A common approach to solve this problem involves the use of the gradient descent method.  An alternative way to compute the FM is to develop a recursive/inductive definition that does not involve optimizing the aforementioned cost function.  In this paper, we propose one such computationally efficient algorithm called the it Grassmann   inductive Frechet mean estimator (GiFME). In developing the recursive solution to find the FM of the given set of points, GiFME exploits the fact that there is a closed form solution to find the FM of two points on the Grassmann.  In the limit as the number of samples tends to infinity, we prove that GiFME converges to the FM (this is called the weak consistency result on the Grassmann manifold).  Further, for the finite sample case, in the limit as the number of sample paths (trials) goes to infinity, we show that GiFME converges to the finite sample FM. Moreover, we present a bound on the geodesic distance between the estimate from GiFME and the true FM. We present several experiments on synthetic and real data sets to demonstrate the performance of GiFME in comparison to the gradient descent based (batch mode) technique. Our goal in these applications is to demonstrate the computational advantage and achieve comparable accuracy to the state-of-the-art.
Multi-View Subspace Clustering
Authors: Hongchang Gao, Feiping Nie, Xuelong. Li, Heng Huang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Multi-view Subspace Clustering

For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.
Structured Feature Selection
Authors: Tian Gao, Ziheng Wang, Qiang Ji
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Dimensionality Reduction, feature selection, General Classification, image-classification, Image Classification

Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery,  that improves the efficiency of state-of-the-art algorithms. Then we theoretically justify three advantages of structured feature selection over traditional feature selection methods. Specifically, we show that the Markov Blanket is the minimum feature set that retains the maximal mutual information and also gives the lowest Bayes classification error. Then we apply structured feature selection to two applications: 1) We introduce a new method that enables STMB to scale up and show the competitive performance of our algorithms on large-scale image classification tasks. 2) We propose a method for structured feature selection to handle hierarchical features and show the proposed method can lead to big performance gain  in facial expression and action unit (AU) recognition tasks.
Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning
Authors: Yan Huang, Wei Wang, Liang Wang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Verification, General Classification, Relation, Vocal Bursts Intensity Prediction

Relation learning is a fundamental operation in many computer vision tasks. Recently, high-order Boltzmann machine and its variants have exhibited the great power of modelling various data relation. However, most of them are unsupervised learning models which are not very discriminative and thus cannot server as a standalone solution to relation learning tasks. In this paper, we explore supervised learning algorithms and propose a new model named Conditional High-order Boltzmann Machine (CHBM), which can be directly used as a bilinear classifier to assign similarity scores for pairwise images. Then, to better deal with complex data relation, we propose a gated version of CHBM which untangles factors of variation by exploiting a set of latent variables to gate classification. We perform four-order tensor factorization for parameter reduction, and present two efficient supervised learning algorithms from the perspectives of being generative and discriminative, respectively. The experimental results of image transformation visualization, binary-way classification and face verification demonstrate that, by performing supervised learning, our models can greatly improve the performance.
Learning Image and User Features for Recommendation in Social Networks
Authors: Xue Geng, Hanwang Zhang, Jingwen Bian, Tat-Seng Chua
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Recommendation Systems

Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and images, and the extremely diverse visual contents of images. To address the challenges, we propose a novel deep model which learns the unified feature representations for both users and images. This is done by transforming the heterogeneous user-image networks into homogeneous low-dimensional representations, which facilitate a recommender to trivially recommend images to users by feature similarity. We also develop a fast online algorithm that can be easily scaled up to large networks in an asynchronously parallel way. We conduct extensive experiments on a representative subset of Pinterest, containing 1,456,540 images and 1,000,000 users. Results of image recommendation experiments demonstrate that our feature learning approach significantly outperforms other state-of-the-art recommendation methods.
Dual-Feature Warping-Based Motion Model Estimation
Authors: Shiwei Li, Lu Yuan, Jian Sun, Long Quan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Stitching, model, Video Stabilization

To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-terms (keypoint correspondences). In some low-texture environments (e.g., indoor) where keypoint feature is insufficient or unreliable, the warping model is often erroneously estimated.  In this paper we propose a simple and effective approach by considering both keypoint and line segment correspondences as data-term. Line segment is a prominent feature in artificial environments and it can supply sufficient geometrical and structural information of scenes, which not only helps guild to a correct warp in low-texture condition, but also prevents the undesired distortion induced by warping. The combination aims to complement each other and benefit for a wider range of scenes. Our method is general and can be ported to many existing applications. Experiments demonstrate that using dual-feature yields more robust and accurate result especially for those low-texture images.
Local Subspace Collaborative Tracking
Authors: Lin Ma, Xiaoqin Zhang, Weiming Hu, Junliang Xing, Jiwen Lu, Jie zhou
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Tracking, Visual Tracking

Subspace models have been widely used for appearance based object tracking. Most existing subspace based trackers employ a linear subspace to represent object appearances, which are not accurate enough to model large variations of objects. To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances. First, we retain a set of key samples and compute a set of local subspaces for each key sample. Then, we construct a hyper sphere to represent the local nonlinear subspace for each key sample. The hyper sphere of one key sample passes the local key samples and also is tangent to the local linear subspace of the specific key sample. In this way, we are able to represent the nonlinear distribution of the key samples and also approximate the local linear subspace near the specific key sample, so that local distributions of the samples can be represented more accurately. Experimental results on challenging video sequences demonstrate the effectiveness of our method.
SpeDo: 6 DOF Ego-Motion Sensor Using Speckle Defocus Imaging
Authors: Kensei Jo, Mohit Gupta, Shree K. Nayar
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Motion Estimation, Optical Flow Estimation, Position

Sensors that measure their motion with respect to the surrounding environment (ego-motion sensors) can be broadly classified into two categories. First is inertial sensors such as accelerometers. In order to estimate position and velocity, these sensors integrate the measured acceleration, which often results in accumulation of large errors over time. Second, camera-based approaches such as SLAM that can measure position directly, but their performance depends on the surrounding scene's properties. These approaches cannot function reliably if the scene has low frequency textures or small depth variations. We present a novel ego-motion sensor called SpeDo that addresses these fundamental limitations. SpeDo is based on using coherent light sources and cameras with large defocus. Coherent light, on interacting with a scene, creates a high frequency interferometric pattern in the captured images, called speckle. We develop a theoretical model for speckle flow (motion of speckle as a function of sensor motion), and show that it is quasi-invariant to surrounding scene's properties. As a result, SpeDo can measure ego-motion (not derivative of motion) simply by estimating optical flow at a few image locations. We have built a low-cost and compact hardware prototype of SpeDo and demonstrated high precision 6 DOF ego-motion estimation for complex trajectories in scenarios where the scene properties are challenging (e.g., repeating or no texture) as well as unknown.
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage
Authors: Hongteng Xu, Yang Zhou, Weiyao Lin, Hongyuan Zha
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Anomaly Detection, Clustering, Trajectory Clustering

This paper proposes a shrinkage-based framework for unsupervised trajectory clustering. Facing to the challenges of trajectory clustering, e.g., large variations within a cluster and ambiguities across clusters, we first introduce an adaptive multi-kernel-based estimation process to estimate the `shrunk' positions and speeds of trajectories' points. This kernel-based estimation effectively leverages both multiple structural information within a trajectory and the local motion patterns across multiple trajectories, such that the discrimination of the shrunk point can be properly increased. We further introduce a speed-regularized optimization process, which utilizes the estimated speeds to regularize the optimal shrunk points, so as to guarantee the smoothness and the discriminative pattern of the final shrunk trajectory. Using our approach, the variations among similar trajectories can be reduced while the boundaries between different clusters are enlarged. Experimental results demonstrate that our approach is superior to the state-of-art approaches on both clustering accuracy and robustness. Besides, additional experiments further reveal the effectiveness of our approach when applied to trajectory analysis applications such as anomaly detection.
TRIC-track: Tracking by Regression With Incrementally Learned Cascades
Authors: Xiaomeng Wang, Michel Valstar, Brais Martinez, Muhammad Haris Khan, Tony Pridmore
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Incremental Learning, regression

This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object's structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark.
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment
Authors: Huijun Di, Qingxuan Shi, Feng Lv, Ming Qin, Yao Lu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Motion Estimation, Motion Segmentation, Optical Flow Estimation

Our goal is to estimate contour flow (the contour pairs with consistent point correspondence) from inconsistent contours extracted independently in two video frames. We formulate the contour flow estimation locally as a motion segmentation problem where motion patterns grouped from optical flow field are exploited for local correspondence measurement. To solve local ambiguities, contour flow estimation is further formulated globally as a contour alignment problem. We propose a novel two-staged strategy to obtain global consistent point correspondence under various contour transitions such as splitting, merging and branching. The goal of the first stage is to obtain possible accurate contour-to-contour alignments, and the second stage aims to make a consistent fusion of many partial alignments. Such a strategy can properly balance the accuracy and the consistency, which enables a middle-level motion representation to be constructed by just concatenating frame-by-frame contour flow estimation. Experiments prove the effectiveness of our method.
Minimizing Human Effort in Interactive Tracking by Incremental Learning of Model Parameters
Authors: Arridhana Ciptadi, James M. Rehg
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Incremental Learning

We address the problem of minimizing human effort in interactive tracking by learning sequence-specific model parameters. Determining the optimal model parameters for each sequence is a critical problem in tracking. We demonstrate that by using the optimal model parameters for each sequence we can achieve high precision tracking results with significantly less effort. We leverage the sequential nature of interactive tracking to formulate an efficient method for learning model parameters through a maximum margin framework. By using our method we are able to save 60-90% of human effort to achieve high precision on two datasets: the VIRAT dataset and an Infant-Mother Interaction dataset.
A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images
Authors: Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Object Detection, Object, object-detection, Object Detection

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.  Our method relies only on grayscale images since depth cameras fail  on metallic objects; it can handle  poorly textured objects, and cluttered, changing  environments; the pose it  predicts degrades gracefully in  presence  of   large  occlusions.   As  a  result,  by   contrast  with  the state-of-the-art,  our  method  is  suitable  for  practical  Augmented  Reality applications even  in industrial environments.   To be robust to  occlusions, we first learn to detect  some parts of the target object. Our key  idea is to then predict the  3D pose of  each part in  the form of the  2D projections of  a few control points.   The advantages  of this representation  is three-fold:  We can predict the  3D pose  of the  object even when  only one  part is  visible; when several parts are visible,  we can combine them easily to  compute a better pose of the object;  the 3D pose we  obtain is usually very accurate,  even when only few parts are visible.