pwc-archive/papers-with-abstracts huggingface dataset:
Low-Rank-Sparse Subspace Representation for Robust Regression
Authors: Yongqiang Zhang, Daming Shi, Junbin Gao, Dansong Cheng
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: regression

Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces. However, few of the existing low-rank regression methods can handle the outliers/noise lying on the sparsely corrupted disjoint subspaces. To address this issue, we propose a low-rank-sparse subspace representation for robust regression, hereafter referred to as LRS-RR in this paper. The main contribution include the following: (1) Unlike most of the existing regression methods, we propose an approach with two phases of low-rank-sparse subspace recovery and regression optimization being carried out simultaneously;(2) we also apply the linearized alternating direction method with adaptive penalty to solved the formulated LRS-RR problem and prove the convergence of the algorithm and analyze its complexity; (3) we demonstrate the efficiency of our method for the high-dimensional corrupted data on both synthetic data and two benchmark datasets against several state-of-the-art robust methods.
AGA: Attribute-Guided Augmentation
Authors: Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Data Augmentation, Decoder, Object Recognition, Scene Recognition, Transfer Learning

We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that  learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.
Lean Crowdsourcing: Combining Humans and Machines in an Online System
Authors: Steve Branson, Grant van Horn, Pietro Perona
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

We introduce a method to greatly reduce the amount of redundant annotations required when crowdsourcing annotations such as bounding boxes, parts, and class labels. For example, if two Mechanical Turkers happen to click on the same pixel location when annotating a part in a given image--an event that is very unlikely to occur by random chance--, it is a strong indication that the location is correct. A similar type of confidence can be obtained if a single Turker happened to agree with a computer vision estimate. We thus incrementally collect a variable number of worker annotations per image based on online estimates of confidence. This is done using a sequential estimation of risk over a probabilistic model that combines worker skill, image difficulty, and an incrementally trained computer vision model. We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations. We show that our method can reduce annotation time by a factor of 4-11 for binary filtering of websearch results, 2-4 for annotation of boxes of pedestrians in images, while in many cases also reducing annotation error. We will make an end-to-end version of our system publicly available.
Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning
Authors: Yuhui Quan, Yan Huang, Hui Ji
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Dictionary Learning, Dynamic Texture Recognition

Dynamic textures (DTs) are video sequences with stationary properties, which exhibit repetitive patterns over space and time. This paper aims at investigating the sparse coding based approach to characterizing local DT patterns for recognition. Owing to the high dimensionality of DT sequences, existing dictionary learning algorithms are not suitable for our purpose due to their high computational costs as well as poor scalability. To overcome these obstacles, we proposed a structured tensor dictionary learning method for sparse coding, which learns a dictionary structured with orthogonality and separability. The proposed method is very fast and more scalable to high-dimensional data than the existing ones. In addition, based on the proposed dictionary learning method, a DT descriptor is developed, which has better adaptivity, discriminability and scalability than the existing approaches. These advantages are demonstrated by the experiments on multiple datasets.
Local Convolutional Features With Unsupervised Training for Image Retrieval
Authors: Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Stereo Matching, Stereo Matching Hand

Patch-level descriptors underlie several important computer vision tasks, such as  stereo-matching or content-based image retrieval.  We introduce a deep convolutional architecture that yields patch-level descriptors,  as an alternative to the popular SIFT descriptor for image retrieval.   The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel ``RomePatches'' dataset. Patch-CKN descriptors yield competitive results compared to supervised CNN alternatives on patch and image retrieval.
RIDE: Reversal Invariant Descriptor Enhancement
Authors: Lingxi Xie, Jingdong Wang, Weiyao Lin, Bo Zhang, Qi Tian
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition

In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample. Since handcrafted descriptors such as SIFT are not reversal invariant, the stability of image representation based on them is consequently limited. A popular solution is to augment the datasets by adding a left-right reversed copy for each original image. This strategy improves recognition accuracy to some extent, but also brings the price of almost doubled time and memory consumptions.  In this paper, we present RIDE (Reversal Invariant Descriptor Enhancement) for fine-grained object recognition. RIDE is a generalized algorithm which cancels out the impact of image reversal by estimating the orientation of local descriptors, and guarantees to produce the identical representation for an image and its left-right reversed copy. Experimental results reveal the consistent accuracy gain of RIDE with various types of descriptors. We also provide insightful discussions on the working mechanism of RIDE and its generalization to other applications.
Discrete Tabu Search for Graph Matching
Authors: Kamil Adamczewski, Yumin Suh, Kyoung Mu Lee
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Graph Matching

Graph matching is a fundamental problem in computer vision. In this paper, we propose a novel graph matching algorithm based on tabu search. The proposed method solves graph matching problem by casting it into an equivalent weighted maximum clique problem of the corresponding association graph, which we further penalize through introducing negative weights. Subsequent tabu search optimization allows for overcoming the convention of using positive weights. The method's distinct feature is that it utilizes the history of search to make more strategic decisions while looking for the optimal solution, thus effectively escaping local optima and in practice achieving superior results. The proposed method, unlike the existing algorithms, enables direct optimization in the original discrete space while encouraging rather than artificially enforcing hard one-to-one constraint, thus resulting in better solution. The experiments demonstrate the robustness of the algorithm in a variety of settings, presenting the state-of-the-art results. The code is available at http://cv.snu.ac.kr/research/ DTSGM/
Discriminative Learning of Deep Convolutional Feature Point Descriptors
Authors: Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, Francesc Moreno-Noguer
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Satellite Image Classification

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify.  By using the L2 distance during both training and testing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available.
Learning Where to Position Parts in 3D
Authors: Marco Pedersoli, Tinne Tuytelaars
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection, Pose Estimation, Position

A common issue in deformable object detection is finding a good way to position the parts. This issue is even more outspoken when considering detection and pose estimation for 3D objects, where parts should be placed in a three-dimensional space. Some methods extract the 3D shape of the object from 3D CAD models. This limits their applicability to categories for which such models are available. Others represent the object with a predefined and simple shape (e.g. a cuboid). This extends the applicability of the model, but in many cases the pre-defined shape is too simple to properly represent the object in 3D. In this paper we propose a new method for the detection and pose estimation of 3D objects, that does not use any 3D CAD model or other 3D information. Starting from a simple and general 3D shape, we learn in a weakly supervised manner the 3D part locations that best fit the training data. As this method builds on a iterative estimation of the part locations, we introduce several speedups to make the method fast enough for practical experiments. We evaluate our model for the detection and pose estimation of faces and cars. Our method obtains results comparable with the state of the art, it is faster than most of the other approaches and does not need any additional 3D information.
Query Adaptive Similarity Measure for RGB-D Object Recognition
Authors: Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang, Yong Rui
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Recognition

This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition. Despite of the impressive top-5 accuracies achieved by existing methods, their top-1 accuracies are not very satisfactory. The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem. To address these problems, this paper first proposes a new similarity measure based on dense matching, through which objects in comparison are warped and aligned, to better tolerate variations. Towards RGB and depth fusion, we argue that a constant and golden weight doesn't exist. The two modalities have varying contributions when comparing objects from different categories. To capture such a dynamic characteristic, a group of matchers equipped with various fusion weights is constructed, to explore the responses of dense matching under different fusion configurations. All the response scores are finally merged following a learning-to-combination way, which provides quite good generalization ability in practice. The proposed approach win the best results on several public benchmarks, e.g., achieves 92.7% top-1 test accuracy on the Washington RGB-D object dataset, with a 5.1% improvement over the state-of-the-art.
Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines
Authors: Chao Sui, Mohammed Bennamoun, Roberto Togneri
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: speech-recognition, Speech Recognition, Visual Speech Recognition

This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM). Unlike all existing visual feature extraction techniques which solely extracts features from video sequences, our method is able to explore both acoustic information and visual information to learn a better visual feature representation in the training stage. During the test stage, instead of using both audio and visual signals, only the videos are used for generating the missing audio feature, and both the given visual and given audio features are used to obtain a joint representation. We carried out our experiments on a large scale audio-visual data corpus, and experimental results show that our proposed techniques outperforms the performance of the hadncrafted features and features learned by other commonly used deep learning techniques.
Cluster-Based Point Set Saliency
Authors: Flora Ponjou Tasse, Jiri Kosinka, Neil Dodgson
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Interest Point Detection, Keypoint Detection, Saliency Detection

We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model  by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using  any topological information.
A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms
Authors: Shida Beigpour, Andreas Kolb, Sven Kunz
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Benchmarking, Image Generation

In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information.  Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.
PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures
Authors: Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images. Our technique leverages the PatchMatch algorithm for finding k-nearest-neighbor (kNN) correspondences in an image. We use these kNNs to recover an initial estimate of the 2D wallpaper basis vectors, and seed vertices of the texture lattice. We iteratively expand this lattice by solving an MRF optimization problem. We show that we can discretize the space of good solutions for the MRF using the kNNs, allowing us to efficiently and accurately optimize the MRF energy function using the Particle Belief Propagation algorithm. We demonstrate our technique on a benchmark NRT dataset containing a wide range of images with geometric and photometric variations, and show that our method clearly outperforms the state of the art in terms of both texel detection rate and texel localization score.
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models
Authors: Jia Li, Changqun Xia, Yafei Song, Shu Fang, Xiaowu Chen
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In the past decades, hundreds of saliency models have been proposed for fixation prediction, along with dozens of evaluation metrics. However, existing metrics, which are often heuristically designed, may draw conflict conclusions in comparing saliency models. As a consequence, it becomes somehow confusing on the selection of metrics in comparing new models with state-of-the-arts. To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models. Instead of heuristically designing such a metric, we first conduct extensive subjective tests to find how saliency maps are assessed by the human-being. Based on the user data collected in the tests, nine representative evaluation metrics are directly compared by quantizing their performances in assessing saliency maps. Moreover, we propose to learn a data-driven metric by using Convolutional Neural Network. Compared with existing metrics, experimental results show that the data-driven metric performs the most consistently with the human-being in evaluating saliency maps as well as saliency models.
A Matrix Decomposition Perspective to Multiple Graph Matching
Authors: Junchi Yan, Hongteng Xu, Hongyuan Zha, Xiaokang Yang, Huanxi Liu, Stephen Chu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Graph Matching

Graph matching has a wide spectrum of real-world applications and in general is known NP-hard. In many vision tasks, one realistic problem arises for finding the global node mappings across a batch of corrupted weighted graphs. This paper is an attempt to connect graph matching, especially multi-graph matching to the matrix decomposition model and its relevant on-the-shelf convex optimization algorithms. Our method aims to extract the common inliers and their synchronized permutations from disordered weighted graphs in the presence of deformation and outliers. Under the proposed framework, several variants can be derived in the hope of accommodating to other types of noises. Experimental results on both synthetic data and real images empirically show that the proposed paradigm exhibits several interesting behaviors and in many cases performs competitively with the state-of-the-arts.
Fast and Effective L0 Gradient Minimization by Region Fusion
Authors: Rang M. H. Nguyen, Michael S. Brown
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Mesh Denoising, Denoising, Image Denoising, Image Enhancement

L_0 gradient minimization can be applied to an input signal to control the number of non-zero gradients.  This is useful in reducing small gradients generally associated with signal noise, while preserving important signal features.  In computer vision, L_0 gradient minimization has found applications in image denoising, 3D mesh denoising, and image enhancement.  Minimizing the L_0 norm, however, is an NP-hard problem because of its non-convex property. As a result, existing methods rely on approximation strategies to perform the minimization.  In this paper, we present a new method to perform L_0 gradient minimization that is fast and effective. Our method uses a descent approach based on region fusion that converges faster than other methods while providing a better approximation of the optimal L_0 norm.  In addition, our method can be applied to both 2D images and 3D mesh topologies.  The effectiveness of our approach is demonstrated on a number of examples.
Generic Promotion of Diffusion-Based Salient Object Detection
Authors: Peng Jiang, Nuno Vasconcelos, Jingliang Peng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this work, we propose a generic scheme to promote any diffusion-based salient object detection algorithm by original ways to re-synthesize the diffusion matrix and construct the seed vector. We first make a novel analysis of the working mechanism of the diffusion matrix, which reveals the close relationship between saliency diffusion and spectral clustering. Following this analysis, we propose to re-synthesize the diffusion matrix from the most discriminative eigenvectors after adaptive re-weighting. Further, we propose to generate the seed vector based on the readily available diffusion maps, avoiding extra computation for color-based seed search. As a particular instance, we use inverse normalized Laplacian matrix as the original diffusion matrix and promote the corresponding salient object detection algorithm, which leads to superior performance as experimentally demonstrated.
Nighttime Haze Removal With Glow and Multiple Light Colors
Authors: Yu Li, Robby T. Tan, Michael S. Brown
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

This paper focuses on dehazing nighttime images.  Most existing dehazing methods use models that are formulated to describe haze in daytime.  Daytime models assume a single uniform light color attributed to a light source not directly visible in the scene.  Nighttime scenes, however, commonly include visible lights sources with varying colors. These light sources also often introduce noticeable amounts of glow that is not present in daytime haze.   To address these effects, we introduce a new nighttime haze model that accounts for the varying light sources and their glow. Our model is a linear combination of three terms: the direct transmission, airlight and glow. The glow term represents light from the light sources that is scattered around before reaching the camera.  Based on the model, we propose a framework that first reduces the effect of the glow in the image, resulting in a nighttime image that consists of direct transmission and airlight only.  We then compute a spatially varying atmospheric light map that encodes light colors locally. This atmospheric map is used to predict the transmission, which we use to obtain our nighttime scene reflection image. We demonstrate the effectiveness of our nighttime haze model and correction method on a number of examples and compare our results with existing daytime and nighttime dehazing methods' results.