pwc-archive/papers-with-abstracts huggingface dataset:
Extraction of Virtual Baselines From Distorted Document Images Using Curvilinear Projection
Authors: Gaofeng Meng, Zuming Huang, Yonghong Song, Shiming Xiang, Chunhong Pan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The baselines of a document page are a set of virtual horizontal and parallel lines, to which the printed contents of document, e.g., text lines, tables or inserted photos, are aligned. Accurate baseline extraction is of great importance in the geometric correction of curved document images. In this paper, we propose an efficient method for accurate extraction of these virtual visual cues from a curved document image. Our method comes from two basic observations that the baselines of documents do not intersect with each other and that within a narrow strip, the baselines can be well approximated by linear segments. Based upon these observations, we propose a curvilinear projection based method and model the estimation of curved baselines as a constrained sequential optimization problem. A dynamic programming algorithm is then developed to efficiently solve the problem. The proposed method can extract the complete baselines through each pixel of document images in a high accuracy. It is also scripts insensitive and highly robust to image noises, non-textual objects, image resolutions and image quality degradation like blurring and non-uniform illumination. Extensive experiments on a number of captured document images demonstrate the effectiveness of the proposed method.
Robust RGB-D Odometry Using Point and Line Features
Authors: Yan Lu, Dezhen Song
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Visual Odometry

Lighting variation and uneven feature distribution are main challenges for indoor RGB-D visual odometry where color information is often combined with depth information. To meet the challenges, we fuse point and line features to form a robust odometry algorithm. Line features are abundant indoors and less sensitive to lighting change than points. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We prove that fusing points and lines produces smaller motion estimate uncertainty than using either feature type alone. In experiments we compare our method with state-of-the-art methods including a keypoint-based approach and a dense visual odometry  algorithm. Our method outperforms the counterparts under both constant and varying lighting conditions. Specifically, our method achieves an average translational error that is 34.9% smaller than the counterparts, when tested using public datasets.
What Makes Tom Hanks Look Like Tom Hanks
Authors: Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Face Reconstruction, Face Reconstruction

We reconstruct a controllable model of a person from a large photo collection that captures his or her persona, i.e., physical appearance and behavior.  The ability to operate on unstructured photo collections enables modeling a huge number of people, including celebrities and other well photographed people without requiring them to be scanned.  Moreover, we show the ability to drive or puppeteer the captured person B using any other video of a different person A.  In this scenario, B acts out the role of person A, but retains his/her own personality and character.  Our system is based on a novel combination of 3D face reconstruction, tracking, alignment, and multi-texture modeling, applied to the puppeteering problem.  We demonstrate convincing results on a large variety of celebrities derived from Internet imagery and video.
FaceDirector: Continuous Control of Facial Performance in Video
Authors: Charles Malleson, Jean-Charles Bazin, Oliver Wang, Derek Bradley, Thabo Beeler, Adrian Hilton, Alexander Sorkine-Hornung
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Audio-Visual Synchronization, continuous-control, Continuous Control

We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states. As an example, given sad and angry video takes of a scene, our method empowers the movie director to specify arbitrary weighted combinations and smooth transitions between the two takes in post-production. Our contributions include (1) a robust nonlinear audio-visual synchronization technique that exploits complementary properties of audio and visual cues to automatically determine robust, dense spatiotemporal correspondences between takes, and (2) a seamless facial blending approach that provides the director full control to interpolate timing, facial expression, and local appearance, in order to generate novel performances after filming. In contrast to most previous works, our approach operates entirely in image space, avoiding the need of 3D facial reconstruction. We demonstrate that our method can synthesize visually believable performances with applications in emotion transition, performance correction, and timing control.
Synthesizing Illumination Mosaics From Internet Photo-Collections
Authors: Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We propose a framework for the automatic creation of time-lapse mosaics of a given scene. We achieve this by leveraging the illumination variations captured in Internet photo-collections. In order to depict and characterize the illumination spectrum of a scene, our method relies on building discrete representations of the image appearance space through connectivity graphs defined over a pairwise image distance function. The smooth appearance transitions are found as the shortest path in the similarity graph among images, and robust image alignment is achieved by leveraging scene semantics, multi-view geometry, and image warping techniques. The attained results present an insightful and compact visualization of the scene illuminations captured in crowd-sourced imagery.
Hot or Not: Exploring Correlations Between Appearance and Temperature
Authors: Daniel Glasner, Pascal Fua, Todd Zickler, Lihi Zelnik-Manor
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper we explore interactions between the appearance of an outdoor scene and the ambient temperature. By studying statistical correlations between image sequences from outdoor cameras and temperature measurements we identify two interesting interactions. First, semantically meaningful regions such as foliage and reflective oriented surfaces are often highly indicative of the temperature. Second, small camera motions are correlated with the temperature in some scenes. We propose simple scene-specific temperature prediction algorithms which can be used to turn a camera into a crude temperature sensor. We find that for this task, simple features such as local pixel intensities outperform sophisticated, global features such as from a semantically-trained convolutional neural network.
SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs
Authors: Yu Li, Dongbo Min, Michael S. Brown, Minh N. Do, Jiangbo Lu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Optical Flow Estimation

Markov random fields are widely used to model many computer vision problems that can be cast in an energy minimization framework composed of unary and pairwise potentials. While computationally tractable discrete optimizers such as Graph Cuts and belief propagation (BP) exist for multi-label discrete problems, they still face prohibitively high computational challenges when the labels reside in a huge or very densely sampled space. Integrating key ideas from PatchMatch of effective particle propagation and resampling, PatchMatch belief propagation (PMBP) has been demonstrated to have good performance in addressing continuous labeling problems and runs orders of magnitude faster than Particle BP (PBP). However, the quality of the PMBP solution is tightly coupled with the local window size, over which the raw data cost is aggregated to mitigate ambiguity in the data constraint. This dependency heavily influences the overall complexity, increasing linearly with the window size. This paper proposes a novel algorithm called sped-up PMBP (SPM-BP) to tackle this critical computational bottleneck and speeds up PMBP by 50-100 times. The crux of SPM-BP is on unifying efficient filter-based cost aggregation and message passing with PatchMatch-based particle generation in a highly effective way. Though simple in its formulation, SPM-BP achieves superior performance for sub-pixel accurate stereo and optical-flow on benchmark datasets when compared with more complex and task-specific approaches.
Similarity Gaussian Process Latent Variable Model for Multi-Modal Data Analysis
Authors: Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Retrieval

Data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized cross-modal mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy of multi-modal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-linear non-parametric mapping functions and transform heterogeneous data into a shared latent space. We propose multi-modal Similarity Gaussian Process latent variable model (m-SimGP), which learns the nonlinear mapping functions between the intra-modal similarities and latent representation. We further propose multi-modal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the output space. The overall objective functions are solved by simple and scalable gradient decent techniques. The proposed models are robust to content divergence and high-dimensionality in multi-modal representation. They can be applied to various tasks to discover the non-linear correlations and obtain the comparable low-dimensional representation for heterogeneous modalities. On two widely used real-world datasets, we outperform previous approaches for cross-modal content retrieval and cross-modal classification.
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables
Authors: Hossein Hajimirsadeghi, Greg Mori
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Activity Recognition, Group Activity Recognition, Structured Prediction, Temporal Action Localization

Many visual recognition tasks involve modeling variables which are structurally related. Hidden conditional random fields (HCRFs) are a powerful class of models for encoding structure in weakly supervised training examples. This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space. An algorithm is proposed to learn the potential functions of an HCRF as a combination of abstract nonlinear feature functions, expressed by regression models. Consequently, the resulting latent structured model is not restricted to traditional log-linear potential functions or any explicit parameterization. Further, functional optimization helps to avoid direct interactions with the possibly large parameter space of nonlinear models and improves efficiency. As a result, a complex and flexible ensemble method is achieved for structured prediction which can be successfully used in a variety of applications. We validate the effectiveness of this method on tasks such as group activity recognition, human action recognition, and multi-instance learning of video events.
Low Dimensional Explicit Feature Maps
Authors: Ondrej Chum
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Approximating non-linear kernels by finite-dimensional feature maps is a popular approach for speeding up training and evaluation of support vector machines or to encode information into efficient match kernels. We propose a novel method of data independent construction of low dimensional feature maps. The problem is cast as a linear program which jointly considers competing objectives: the quality of the approximation and the dimensionality of the feature map.  For both shift-invariant and homogeneous kernels the proposed method achieves a better approximations at the same dimensionality or comparable approximations at lower dimensionality of the feature map compared with state-of-the-art methods.
Multi-Label Cross-Modal Retrieval
Authors: Viresh Ranjan, Nikhil Rasiwasia, C. V. Jawahar
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Cross-Modal Retrieval, Retrieval

In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations.  In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA), an extension of CCA, for learning shared subspaces taking into account high level semantic information in the form of multi-label annotations. Unlike CCA, ml-CCA does not rely on explicit pairing between modalities, instead it uses the multi-label information to establish correspondences.  This results in a discriminative subspace which is better suited for cross-modal retrieval tasks.  We also present Fast ml-CCA, a computationally efficient version of ml-CCA, which is able to handle large scale datasets.  We show the efficacy of our approach by conducting extensive cross-modal retrieval experiments on three standard benchmark datasets. The results show that the proposed approach achieves state of the art retrieval performance on the three datasets.
Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities
Authors: Antonio L. Rodriguez, Vitor Sequeira
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Classification, General Classification, Prediction, valid

Ferns ensembles offer an accurate and efficient multiclass non-linear classification, commonly at the expense of consuming a large amount of memory. We introduce a two-fold contribution that produces large reductions in their memory consumption. First, an efficient L0 regularised cost optimisation finds a sparse representation of the posterior probabilities in the ensemble by discarding elements with zero contribution to valid responses in the training samples. As a by-product this can produce a prediction accuracy gain that, if required, can be traded for further reductions in memory size and prediction time. Secondly, posterior probabilities are quantised and stored in a memory-friendly sparse data structure. We reported a minimum of 75% memory reduction for different types of classification problems using generative and discriminative ferns ensembles, without increasing prediction time or classification error. For image patch recognition our proposal produced a 90% memory reduction, and improved in several percentage points the prediction accuracy.
Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data
Authors: Tzu Ming Harry Hsu, Wei Yu Chen, Cheng-An Hou, Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Domain Adaptation, General Classification, Unsupervised Domain Adaptation

We address a challenging unsupervised domain adaptation problem with imbalanced cross-domain data. For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain. However, most existing works do not consider the scenarios in which either the label numbers across domains are different, or the data in the source and/or target domains might be collected from multiple datasets. To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains. Experiments on multiple cross-domain visual classification tasks confirm that our method performs favorably against state-of-the-art approaches, especially when imbalanced cross-domain data are presented.
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts
Authors: Je Hyeong Hong, Andrew Fitzgibbon
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Low-Rank Matrix Completion, Matrix Completion

Matrix factorization (or low-rank matrix completion) with missing data is a key computation in many computer vision and machine learning tasks, and is also related to a broader class of nonlinear optimization problems such as bundle adjustment. The problem has received much attention recently, with renewed interest in variable-projection approaches, yielding dramatic improvements in reliability and speed. However, on a wide class of problems, no one approach dominates, and because the various approaches have been derived in a multitude of different ways, it has been difficult to unify them. This paper provides a unified derivation of a number of recent approaches, so that similarities and differences are easily observed. We also present a simple meta-algorithm which wraps any existing algorithm, yielding 100% success rate on many standard datasets. Given 100% success, the focus of evaluation must turn to speed, as 100% success is trivially achieved if we do not care about speed. Again our unification allows a number of generic improvements applicable to all members of the family to be isolated, yielding a unified algorithm that outperforms our re-implementation of existing algorithms, which in some cases already outperform the original authors' publicly available codes.
Learning Binary Codes for Maximum Inner Product Search
Authors: Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, Heng Tao Shen
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Binary coding or hashing techniques are recognized to accomplish efficient near neighbor search, and have thus attracted broad interests in the recent vision and learning studies. However, such studies have rarely been dedicated to Maximum Inner Product Search (MIPS), which plays a critical role in various vision applications. In this paper, we investigate learning binary codes to exclusively handle the MIPS problem. Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting. Specifically, two sets of coding functions are learned such that the inner products between their generated binary codes can reveal the inner products between original data vectors. We also propose an alternative simpler objective which maximizes the correlations between the inner products of the produced binary codes and raw data vectors. In both objectives, the binary codes and coding functions are simultaneously learned without continuous relaxations, which is the key to achieving high-quality binary codes. We evaluate the proposed method, dubbed Asymmetric Inner-product Binary Coding (AIBC), relying on the two objectives on several large-scale image datasets. Both of them are superior to the state-of-the-art binary coding and hashing methods in performing MIPS tasks.
ML-MG: Multi-Label Learning With Missing Labels Using a Mixed Graph
Authors: Baoyuan Wu, Siwei Lyu, Bernard Ghanem
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Missing Labels, Multi-Label Learning

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.
Bayesian Model Adaptation for Crowd Counts
Authors: Bo Liu, Nuno Vasconcelos
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Crowd Counting, Gaussian Processes, model, Multi-Task Learning, Transfer Learning

The problem of transfer learning is considered in the domain of crowd counting. A solution based on Bayesian model adaptation of Gaussian processes is proposed. This is shown to produce intuitive model updates, which are tractable, and lead to an adapted model (predictive distribution) that accounts for all information in both training and adaptation data. The new adaptation procedure achieves significant gains over previous approaches, based on multi-task learning, while requiring much less computation to deploy. This makes it particularly suited for the problem of expanding the capacity of crowd counting camera networks. A large video dataset for the evaluation of adaptation approaches to crowd counting is also introduced. This contains a number of adaptation tasks, involving information transfer across video collected by 1) a single camera under different scene conditions (different times of the day) and 2) video collected from different cameras. Evaluation of the proposed model adaptation procedure in this dataset shows good performance in realistic operating conditions.
An NMF Perspective on Binary Hashing
Authors: Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: GPU, Quantization, Retrieval

The pervasiveness of massive data repositories has led to much interest in efficient methods for indexing, search, and retrieval. For image data, a rapidly developing body of work for these applications shows impressive performance with methods that broadly fall under the umbrella term of Binary Hashing. Given a distance matrix, a binary hashing algorithm solves for a binary code for the given set of examples, whose Hamming distance nicely approximates the original distances. The formulation is non-convex-- so existing solutions adopt spectral relaxations or perform coordinate descent (or quantization) on a surrogate objective that is numerically more tractable. In this paper, we first derive an Augmented Lagrangian approach to optimize the standard binary Hashing objective (i.e.,maintain fidelity with a given distance matrix). With appropriate step sizes, we find that this scheme already yields results that match or substantially outperform state of the art methods on most benchmarks used in the literature. Then, to allow the model to scale to large datasets, we obtain an interesting reformulation of the binary hashing objective as a non negative matrix factorization. Later, this leads to a simple multiplicative updates algorithm -- whose parallelization properties are exploited to obtain a fast GPU based implementation. We give a probabilistic analysis of our initialization scheme and present a range of experiments to show that the method is simple to implement and competes favorably with available methods (both for optimization and generalization).