pwc-archive/papers-with-abstracts huggingface dataset:
Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Authors: George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https://bitbucket.org/deeplab/deeplab-public.
Entropy Minimization for Convex Relaxation Approaches
Authors: Mohamed Souiai, Martin R. Oswald, Youngwook Kee, Junmo Kim, Marc Pollefeys, Daniel Cremers
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Binarization, Image Segmentation, Semantic Segmentation

Despite their enormous success in solving hard combinatorial problems, convex relaxation approaches often suffer from the fact that the computed solutions are far from binary and that subsequent heuristic binarization may substantially degrade the quality of computed solutions.  In this paper, we propose a novel relaxation technique which incorporates the entropy of the objective variable as a measure of relaxation tightness. We show both theoretically and experimentally that augmenting the objective function with an entropy term gives rise to more binary solutions and consequently solutions with a substantially tighter optimality gap.  We use difference of convex function (DC) programming as an efficient and provably convergent solver for the arising convex-concave minimization problem.  We evaluate this approach on three prominent non-convex computer vision challenges: multi-label inpainting, image segmentation and spatio-temporal multi-view reconstruction.  These experiments show that our approach consistently yields better solutions with respect to the original integral optimization problem
Adaptively Unified Semi-Supervised Dictionary Learning With Active Points
Authors: Xiaobo Wang, Xiaojie Guo, Stan Z. Li
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Dictionary Learning

Semi-supervised dictionary learning aims to construct a dictionary by utilizing both labeled and unlabeled data. To enhance the discriminative capability of the learned dictionary, numerous discriminative terms have been proposed by evaluating either the prediction loss or the class separation criterion on the coding vectors of labeled data, but with rare consideration of the power of the coding vectors corresponding to unlabeled data. In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously. By doing so, we integrate the discrimination of dictionary, the induction of classifier to new testing data and the transduction of labels to unlabeled data into a unified framework. To solve the proposed problem, an effective iterative algorithm is designed. Experimental results on a series of benchmark databases show that our method outperforms other state-of-the-art dictionary learning methods in most cases.
A Multiscale Variable-Grouping Framework for MRF Energy Minimization
Authors: Omer Meir, Meirav Galun, Stav Yagev, Ronen Basri, Irad Yavneh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We present a multiscale approach for minimizing the energy associated with Markov Random Fields (MRFs) with energy functions that include arbitrary pairwise potentials. The MRF is represented on a hierarchy of successively coarser scales, where the problem on each scale is itself an MRF with suitably defined potentials. These representations are used to construct an efficient multiscale algorithm that seeks a minimal-energy solution to the original problem. The algorithm is iterative and features a bidirectional crosstalk between fine and coarse representations. We use consistency criteria to guarantee that the energy is nonincreasing throughout the iterative process. The algorithm is evaluated on real-world datasets, achieving competitive performance in relatively short run-times.
Inferring M-Best Diverse Labelings in a Single One
Authors: Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry Vetrov, Carsten Rother
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Diversity, Image Segmentation, Semantic Segmentation

We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.
Convolutional Sparse Coding for Image Super-Resolution
Authors: Shuhang Gu, WangMeng Zuo, Qi Xie, Deyu Meng, Xiangchu Feng, Lei Zhang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

   Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR). Most of the previous SC based SR methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue. Our CSC-SR involves three groups of parameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps; (ii) a  mapping function to predict the high resolution (HR) feature maps from the LR ones; and (iii) a set of filters to reconstruct the HR images from the predicted HR feature maps via simple convolution operations. By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures. Experimental results clearly validate the advantages of CSC over patch based SC in SR application. Compared with state-of-the-art SR methods, the proposed CSC-SR method achieves highly competitive PSNR results, while demonstrating better edge and texture preservation performance.
A Wavefront Marching Method for Solving the Eikonal Equation on Cartesian Grids
Authors: Brais Cancela, Marcos Ortega, Manuel G. Penedo
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

This paper presents a new wavefront propagation method for dealing with the classic Eikonal equation. While classic Dijkstra-like graph-based techniques achieve the solution in O(M log M), they do not approximate the unique physically relevant solution very well. Fast Marching Methods (FMM) were created to efficiently solve the continuous problem. The proposed approximation tries to maintain the complexity, in order to make the algorithm useful in a wide range of contexts. The key idea behind our method is the creation of 'mini wave-fronts', which are combined to propagate the solution. Experimental results show the improvement in the accuracy with respect to the state of the art, while the average computational speed is maintained in O(M log M), similar to the FMM techniques.
A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer
Authors: Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Semantic Segmentation, Stochastic Optimization

Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation. Few other linear algebra problems have a more mature set of numerical routines available and many computer vision libraries leverage such tools extensively. However, the ability to call the underlying solver only as a ``black box'' can often become restrictive. Many `human in the loop' settings in vision frequently exploit supervision from an expert, to the extent that the user can be considered a subroutine in the overall system. In other cases, there is additional domain knowledge, side or even partial information that one may want to incorporate within the formulation. In general, regularizing a (generalized) eigenvalue problem with such side information remains difficult. Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer. We start from an alternative formulation of GEP where the feasibility set of the model involves the Stiefel manifold. The core of this paper presents an end to end stochastic optimization scheme for the resultant problem. We show how this general algorithm enables improved statistical analysis of brain imaging data where the regularizer is derived from other `views' of the disease pathology, involving clinical measurements and other image-derived representations.
Optimizing Expected Intersection-Over-Union With Candidate-Constrained CRFs
Authors: Faruk Ahmed, Dany Tarlow, Dhruv Batra
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the Intersection-over-Union (IoU) measure that is used widely in evaluating image segmentation systems. Currently, there are two dominant approaches: the first approximates the Expected-IoU (EIoU) score as Expected-Intersection-over-Expected-Union (EIoEU); and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions. We begin by asking which approach we should favor for two typical image segmentation tasks. Studying this question leads to two new methods that draw ideas from both existing approaches. Our new methods use the EIoEU approximation paired with high quality candidate solutions. Experimentally we show that our new approaches lead to improved performance on both image segmentation tasks.
Higher-Order Inference for Multi-Class Log-Supermodular Models
Authors: Jian Zhang, Josip Djolonga, Andreas Krause
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Semantic Segmentation, Variational Inference

Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges
Authors: James S. Supancic III, Gregory Rogez, Yi Yang, Jamie Shotton, Deva Ramanan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Hand Pose Estimation, Pose Estimation

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
Adaptive Dither Voting for Robust Spatial Verification
Authors: Xiaomeng Wu, Kunio Kashino
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Quantization, Retrieval

Hough voting in a geometric transformation space allows us to realize spatial verification, but remains sensitive to feature detection errors because of the inflexible quantization of single feature correspondences. To handle this problem, we propose a new method, called adaptive dither voting, for robust spatial verification. For each correspondence, instead of hard-mapping it to a single transformation, the method augments its description by using multiple dithered transformations that are deterministically generated by the other correspondences. The method reduces the probability of losing correspondences during transformation quantization, and provides high robustness as regards mismatches by imposing three geometric constraints on the dithering process. We also propose exploiting the non-uniformity of a Hough histogram as the spatial similarity to handle multiple matching surfaces. Extensive experiments conducted on four datasets show the superiority of our method. The method outperforms its state-of-the-art counterparts in both accuracy and scalability, especially when it comes to the retrieval of small, rotated objects.
Alternating Co-Quantization for Cross-Modal Hashing
Authors: Go Irie, Hiroyuki Arai, Yukinobu Taniguchi
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Cross-Modal Retrieval, Dimensionality Reduction, Quantization, Retrieval

This paper addresses the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. Many unimodal hashing studies have proven that both similarity preservation of data and maintenance of quantization quality are essential for improving retrieval performance with binary hash codes. However, most existing cross-modal hashing methods mainly have focused on the former, and the latter still remains almost untouched. We propose a method to minimize the binary quantization errors, which is tailored to cross-modal hashing. Our approach, named Alternating Co-Quantization (ACQ), alternately seeks binary quantizers for each modality space with the help of connections to other modality data so that they give minimal quantization errors while preserving data similarities. ACQ can be coupled with various existing cross-modal dimension reduction methods such as Canonical Correlation Analysis (CCA) and substantially boosts their retrieval performance in the Hamming space. Extensive experiments demonstrate that ACQ can outperform several state-of-the-art methods, even when it is combined with simple CCA.
Learning Deep Representation With Large-Scale Attributes
Authors: Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Clustering, Object, object-detection, Object Detection

Learning strong feature representations from large scale supervision has achieved remarkable success in computer vision as the emergence of deep learning techniques. It is driven by big visual data with rich annotations. This paper contributes a large-scale object attribute database (The dataset is available on  www.ee.cuhk.edu.hk/ xgwang/ImageNetAttribute.html) that contains  rich attribute annotations (over 300 attributes) for ~180k samples and 494 object classes. Based on the ImageNet object detection dataset, it annotates the rotation, viewpoint, object part location, part occlusion, part existence, common attributes, and class-specific attributes. Then we use this dataset to train deep representations and extensively evaluate how these attributes are useful on the general object detection task. In order to make better use of the attribute annotations, a deep learning scheme is proposed by modeling the relationship of attributes and hierarchically clustering them into semantically meaningful mixture types. Experimental results show that the attributes are helpful in learning better features and improving the object detection accuracy by 2.6% in mAP on the ILSVRC 2014 object detection dataset and 2.4% in mAP on PASCAL VOC 2007 object detection dataset. Such improvement is well generalized across datasets.
Deep Learning Strong Parts for Pedestrian Detection
Authors: Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Deep Learning, Occlusion Handling, Pedestrian Detection

Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (e.g. ImageNet). Although these features are able to handle variations such as poses, viewpoints, and lightings, they may fail when pedestrian images with complex occlusions are present. Occlusion handling is one of the most important problem in pedestrian detection. Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors. DeepParts has several appealing properties. First, DeepParts can be trained on weakly labeled data, i.e. only pedestrian bounding boxes without part annotations are provided. Second, DeepParts is able to handle low IoU positive proposals that shift away from ground truth. Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal. Extensive experiments in Caltech dataset demonstrate the effectiveness of DeepParts, which yields a new state-of-the-art miss rate of 11:89%, outperforming the second best method by 10%.
Top Rank Supervised Binary Coding for Visual Search
Authors: Dongjin Song, Wei Liu, Rongrong Ji, David A. Meyer, John R. Smith
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval

In recent years, binary coding techniques are becoming increasingly popular because of their high efficiency in handling large-scale computer vision applications. It has been demonstrated that supervised binary coding techniques that leverage supervised information can significantly enhance the coding quality, and hence greatly benefit visual search tasks. Typically, a modern binary coding method seeks to learn a group of coding functions which compress data samples into binary codes. However, few methods pursued the coding functions such that the precision at the top of a ranking list according to Hamming distances of the generated binary codes is optimized. In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information. The core idea is to train the disciplined coding functions, by which the mistakes at the top of a Hamming-distance ranking list are penalized more than those at the bottom. To solve such coding functions, we relax the original discrete optimization objective with a continuous surrogate, and derive a stochastic gradient descent to optimize the surrogate objective. To further reduce the training time cost, we also design an online learning algorithm to optimize the surrogate objective more efficiently. Empirical studies based upon three benchmark image datasets demonstrate that the proposed binary coding approach achieves superior image search accuracy over the state-of-the-arts.
BubbLeNet: Foveated Imaging for Visual Discovery
Authors: Kevin Matzen, Noah Snavely
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Deep Learning

We propose a new method for turning an Internet-scale corpus of categorized images into a small set of human-interpretable discriminative visual elements using powerful tools based on deep learning. A key challenge with deep learning methods is generating human-interpretable models. To address this, we propose a new technique that uses bubble images -- images where most of the content has been obscured -- to identify spatially localized, discriminative content in each image. By modifying the model training procedure to use both the source imagery and these bubble images, we can arrive at final models which retain much of the original classification performance, but are much more amenable to identifying interpretable visual elements. We apply our algorithm to a wide variety of datasets, including two new Internet-scale datasets of people and places, and show applications to visual mining and discovery. Our method is simple, scalable, and produces visual elements that are highly representative compared to prior work.
PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables
Authors: Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Quantization

We propose the product quantization table (PQTable), a product quantization-based hash table that is fast and requires neither parameter tuning nor training steps. The PQTable produces exactly the same results as a linear PQ search, and is 10^2 to 10^5 times faster when tested on the SIFT1B data. In addition, although state-of-the-art performance can be achieved by previous inverted-indexing-based approaches, such methods do require manually designed parameter setting and much training, whereas our method is free from them. Therefore, PQTable offers a practical and useful solution for real-world problems.