pwc-archive/papers-with-abstracts huggingface dataset:
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification
Authors: Senjian An, Munawar Hayat, Salman H. Khan, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Classification, General Classification

 To find the optimal nonlinear separating boundary with maximum margin in the input data space, this paper proposes Contractive Rectifier Networks (CRNs), wherein the hidden-layer transformations are restricted to be contraction mappings. The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer. The training of the proposed CRNs is formulated as a linear support vector machine (SVM) in the output layer, combined with two or more contractive hidden layers. Effective algorithms have been proposed to address the optimization challenges arising from contraction constraints. Experimental results on MNIST, CIFAR-10, CIFAR-100 and MIT-67 datasets demonstrate that the proposed contractive rectifier networks consistently outperform their conventional unconstrained rectifier network counterparts.
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization
Authors: Zhe Xu, Shaoli Huang, Ya zhang, DaCheng Tao
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition

We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework. Although both schemes have been widely used to boost recognition performance, due to the difficulty in acquiring detailed part annotations, strongly supervised fine-grained datasets are usually too small to keep pace with the rapid evolution of CNN architectures. In this paper, we solve this problem by exploiting inexhaustible web data. The proposed method improves classification accuracy in two ways: more discriminative CNN feature representations are generated using a training set augmented by collecting a large number of part patches from weakly supervised web images; and more robust object classifiers are learned using a multi-instance learning algorithm jointly on the strong and weak datasets. Despite its simplicity, the proposed method delivers a remarkable performance improvement on the CUB200-2011 dataset compared to baseline part-based R-CNN methods, and achieves the highest accuracy on this dataset even in the absence of test image annotations.
Learning Common Sense Through Visual Abstraction
Authors: Ramakrishna Vedantam, Xiao Lin, Tanmay Batra, C. Lawrence Zitnick, Devi Parikh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Common Sense Reasoning

Common sense is essential for building intelligent machines. While some commonsense knowledge is explicitly stated in human-generated text and can be learnt by mining the web, much of it is unwritten. It is often unnecessary and even unnatural to write about commonsense facts. While unwritten, this commonsense knowledge is not unseen! The visual world around us is full of structure modeled by commonsense knowledge. Can machines learn common sense simply by observing our visual world? Unfortunately, this requires automatic and accurate detection of objects, their attributes, poses, and interactions between objects, which remain challenging problems. Our key insight is that while visual common sense is depicted in visual content, it is the semantic features that are relevant and not low-level pixel information. In other words, photorealism is not necessary to learn common sense. We explore the use of human-generated abstract scenes made from clipart for learning common sense. In particular, we reason about the plausibility of an interaction or relation between a pair of nouns by measuring the similarity of the relation and nouns with other relations and nouns we have seen in abstract scenes. We show that the commonsense knowledge we learn is complementary to what can be learnt from sources of text.
Square Localization for Efficient and Accurate Object Detection
Authors: Cewu Lu, Yongyi Lu, Hao Chen, Chi-Keung Tang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection, Object Localization

The key contribution of this paper is the compact square object localization, which relaxes the exhaustive sliding window from testing all windows of different combinations of aspect ratios. Square object localization is category scalable. By using a binary search strategy, the number of scales to test is further reduced empirically to only O(log(minfH;Wg)) rounds of sliding CNNs, where H and W are respectively the image height and width. In the training phase, square CNN models and object co-presence priors are learned. In the testing phase, sliding CNN models are applied which produces a set of response maps that can be effectively filtered by the learned co-presence prior to output the final bounding boxes for localizing an object. We performed extensive experimental evaluation on the VOC 2007 and 2012 datasets to demonstrate that while efficient,square localization can output precise bounding boxes to improve the final detection result.
Box Aggregation for Proposal Decimation: Last Mile of Object Detection
Authors: Shu Liu, Cewu Lu, Jiaya Jia
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection

Regions-with-convolutional-neural-network (RCNN) is now a commonly employed object detection pipeline. Its main steps, i.e., proposal generation and convolutional neural network (CNN) feature extraction, have been intensively investigated. We focus on the last step of the system to aggregate thousands of scored box proposals into final object prediction, which we call proposal decimation. We show this step can be enhanced with a very simple box aggregation function by considering statistical properties of proposals with respect to ground truth objects. Our method is with extremely light-weight computation, while it yields an improvement of 3.7% in mAP on PASCAL VOC 2007 test. We explain why it works using some statistics in this paper.
Semantic Segmentation With Object Clique Potential
Authors: Xiaojuan Qi, Jianping Shi, Shu Liu, Renjie Liao, Jiaya Jia
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Segmentation, Semantic Segmentation

In this paper, we propose an object clique potential for semantic segmentation. Our object clique potential addresses the misclassified object-part issues arising in solutions based on fully-connected networks. Our object clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly small size, making our method consume notably less computation. Regarding system design and model formation, our object clique potential can be regarded as a functionally complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning
Authors: Ragav Venkatesan, Parag Chandakkar, Baoxin Li
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Multiple Instance Learning

 Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances). A plethora of approaches have been developed to solve this problem in the past years. Popular methods include the diverse density, MILIS and DD-SVM. While having been widely used, these methods, particularly those in computer vision have attempted fairly sophisticated solutions to solve certain unique and particular configurations of the MIL space.      In this paper, we analyze the MIL feature space using modified versions of traditional non-parametric techniques like the Parzen window and k-nearest-neighbour, and develop a learning approach employing distances to k-nearest neighbours of a point in the feature space. We show that these methods work as well, if not better than most recently published methods on benchmark datasets. We compare and contrast our analysis with the well-established diverse-density approach and its variants in recent literature, using benchmark datasets including the Musk, Andrews' and Corel datasets, along with a diabetic retinopathy pathology diagnosis dataset. Experimental results demonstrate that, while enjoying an intuitive interpretation and supporting fast learning, these method have the potential of delivering improved performance even for complex data arising from real-world applications.
Structural Kernel Learning for Large Scale Multiclass Object Co-Detection
Authors: Zeeshan Hayder, Xuming He, Mathieu Salzmann
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection

Exploiting contextual relationships across images has recently proven key to improve object detection. The resulting object co-detection algorithms, however, fail to exploit the correlations between multiple classes and, for scalability reasons are limited to modeling object instance similarity with relatively low-dimensional hand-crafted features. Here, we address the problem of multiclass object co-detection for large scale datasets. To this end, we formulate co-detection as the joint multiclass labeling of object candidates obtained in a class-independent manner. To exploit the correlations between objects, we build a fully-connected CRF on the candidates, which explicitly incorporates both geometric layout relations across object classes and similarity relations across multiple images. We then introduce a structural boosting algorithm that lets us exploits rich, high-dimensional deep network features to learn object similarity within our fully-connected CRF. Our experiments on PASCAL VOC 2007 and 2012 evidences the benefits of our approach over object detection with RCNN, single-image CRF methods and state-of-the-art co-detection algorithms.
Common Subspace for Model and Similarity: Phrase Learning for Caption Generation From Images
Authors: Yoshitaka Ushiku, Masataka Yamaguchi, Yusuke Mukuta, Tatsuya Harada
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Caption Generation, Descriptive

Generating captions to describe images is a fundamental problem that combines computer vision and natural language processing. Recent works focus on descriptive phrases, such as "a white dog" to explain the visual composites of an input image. The phrases can not only express objects, attributes, events, and their relations but can also reduce visual complexity. A caption for an input image can be generated by connecting estimated phrases using a grammar model. However, because phrases are combinations of various words, the number of phrases is much larger than the number of single words. Consequently, the accuracy of phrase estimation suffers from too few training samples per phrase.  In this paper, we propose a novel phrase-learning method: Common Subspace for Model and Similarity (CoSMoS). In order to overcome the shortage of training samples, CoSMoS obtains a subspace in which (a) all feature vectors associated with the same phrase are mapped as mutually close, (b) classifiers for each phrase are learned, and (c) training samples are shared among co-occurring phrases. Experimental results demonstrate that our system is more accurate than those in earlier work and that the accuracy increases when the dataset from the web increases.
3D-Assisted Feature Synthesis for Novel Views of an Object
Authors: Hao Su, Fan Wang, Eric Yi, Leonidas J. Guibas
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval, Object, Retrieval

Comparing two images from different views has been a long-standing challenging problem in computer vision, as visual features are not stable under large view point changes. In this paper, given a single input image of an object, we synthesize its features for other views, leveraging an existing modestly-sized 3D model collection of related but not identical objects.To accomplish this, we study the relationship of image patches between different views of the same object, seeking what we call surrogate patches -- patches in one view whose feature content predicts well the features of a patch in another view. Based upon these surrogate relationships, we can create feature sets for all views of the latent object on a per patch basis, providing us an augmented multi-view representation of the object. We provide theoretical and empirical analysis of the feature synthesis process, and evaluate the augmented features in fine-grained image retrieval/recognition and instance retrieval tasks. Experimental results show that our synthesized features do enable view-independent comparison between images and perform significantly better than other traditional approaches in this respect.
Lost Shopping! Monocular Localization in Large Indoor Spaces
Authors: Shenlong Wang, Sanja Fidler, Raquel Urtasun
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Text Detection, Translation

In this paper we propose a novel approach to  localization  in very large indoor spaces (i.e., 200+ store shopping malls)  that takes   a single image and a floor plan of the environment as input.  We formulate  the localization problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop's names in the image with precise bounding boxes), shop facade segmentation, as well as   camera's  rotation and translation within the entire shopping mall. The power of our approach is that it does not use any prior  information about appearance  and instead  exploits text detections corresponding to the shop names. This makes our method applicable to a variety of domains and robust to store appearance variation across countries, seasons, and illumination conditions. We demonstrate the performance of our approach in a new dataset we collected of two very large shopping malls, and show the power of holistic reasoning.
Camera Pose Voting for Large-Scale Image-Based Localization
Authors: Bernhard Zeisl, Torsten Sattler, Marc Pollefeys
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Camera Pose Estimation, Image-Based Localization, Pose Estimation

Image-based localization approaches aim to determine the camera pose from which an image was taken. Finding correct 2D-3D correspondences between query image features and 3D points in the scene model becomes harder as the size of the model increases. Current state-of-the-art methods therefore combine elaborate matching schemes with camera pose estimation techniques that are able to handle large fractions of wrong matches. In this work we study the benefits and limitations of spatial verification compared to appearance-based filtering. We propose a voting-based pose estimation strategy that exhibits O(n) complexity in the number of matches and thus facilitates to consider much more matches than previous approaches - whose complexity grows at least quadratically. This new outlier rejection formulation enables us to evaluate pose estimation for 1-to-many matches and to surpass the state-of-the-art. At the same time, we show that using more matches does not automatically lead to a better performance.
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking
Authors: Thibaut Durand, Nicolas Thome, Matthieu Cord
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification, image-classification, Image Classification, Multi-class Classification, Weakly-supervised Learning

In this work, we propose a novel Weakly Supervised  Learning (WSL) framework dedicated to learn discriminative part detectors from images annotated with a global label. Our WSL method encompasses three main contributions. Firstly, we introduce a new structured output latent variable model, Minimum mAximum lateNt sTRucturAl SVM (MANTRA), which prediction relies on a pair of latent variables: h^+ (resp. h^-) provides positive (resp. negative) evidence for a given output y. Secondly, we instantiate MANTRA for two different visual recognition tasks: multi-class classification and ranking. For ranking, we propose efficient solutions to exactly solve the inference and the loss-augmented problems. Finally, extensive experiments highlight the relevance of the proposed method: MANTRA outperforms state-of-the art results on five different datasets.
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
Authors: Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis Decoste, Wei Di, Yizhou Yu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: image-classification, Image Classification, Object Recognition

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HD-CNN training, component-wise pretraining is followed by global finetuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers  and layer parameter compression make HD-CNNs scalable for large-scale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different HD-CNNs and they lower the top-1 error of the standard CNNs by 2.65%, 3.1% and 1.1%, respectively
Learning The Structure of Deep Convolutional Networks
Authors: Jiashi Feng, Trevor Darrell
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: image-classification, Image Classification, Semi-Supervised Image Classification

In this work,  we develop a novel method for automatically learning aspects of the structure of a deep model, in order to improve its  performance, especially when labeled training data are scarce. We propose a new convolutional neural network model with the Indian Buffet Process (IBP) prior, termed ibpCNN. The ibpCNN automatically adapts its structure to provided training data,  achieves an optimal balance among model complexity, data fidelity and training loss, and thus  offers  better generalization performance.   The proposed ibpCNN captures complicated data distribution in an unsupervised generative way. Therefore, ibpCNN can exploit unlabeled data -- which can be collected at low cost -- to learn its  structure. After determining the structure, ibpCNN further learns its parameters according to specified tasks, in an end-to-end fashion, and produces  discriminative yet compact representations.    We evaluate the  performance of ibpCNN, on fully- and semi-supervised image classification tasks; ibpCNN surpasses standard CNN models on benchmark datasets, with much smaller size and higher efficiency.
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
Authors: Chun-Guang Li, Zhouchen Lin, Honggang Zhang, Jun Guo
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels. While such a two-stage framework has been successful in many applications, solving two subproblems separately only once is still suboptimal because it does not fully exploit the correlation between the affinity and the labels. In this paper, we formulate the two stages of SSL into a unified optimization framework, which learns both the affinity matrix and the unknown labels simultaneously. In the unified framework, both the given labels and the estimated labels are used to learn the affinity matrix and to infer the unknown labels. We solve the unified optimization problem via an alternating direction method of multipliers combined with label propagation. Extensive experiments on a synthetic data set and several benchmark data sets demonstrate the effectiveness of our approach.
Learning to Rank Based on Subsequences
Authors: Basura Fernando, Efstratios Gavves, Damien Muselet, Tinne Tuytelaars
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Learning-To-Rank

We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analysing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequences instead. These sub-sequences contain useful structural ranking information that leads to better learnability during training and better generalization during testing. By exploiting sub-sequences, the proposed MidRank improves ranking accuracy considerably on an extensive array of image ranking applications and datasets.
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition
Authors: Chengjiang Long, Gang Hua
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Active Learning, Bayesian Inference, General Classification, Reinforcement Learning, Reinforcement Learning (RL)

Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. However, less research attention has been focused on multi-class active learning. In this paper, we propose a novel Gaussian process classifier model with multiple annotators for multi-class visual recognition. Expectation propagation (EP) is adopted for efficient approximate Bayesian inference of our probabilistic model for classification. Based on the EP approximation inference, a generalized Expectation Maximization (GEM) algorithm is derived to estimate both the parameters for instances and the quality of each individual annotator. Also, we incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality annotators, which better explores the trade-off between exploitation and exploration. The experiments clearly demonstrate the efficacy of the proposed model.
Additive Nearest Neighbor Feature Maps
Authors: Zhenzhen Wang, Xiao-Tong Yuan, Qingshan Liu, Shuicheng Yan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper, we present a concise framework to approximately construct feature maps for nonlinear additive kernels such as the Intersection, Hellinger's, and Chi^2 kernels. The core idea is to construct for each individual feature a set of anchor points and assign to every query the feature map of its nearest neighbor or the weighted combination of those of its k-nearest neighbors in the anchors. The resultant feature maps can be compactly stored by a group of nearest neighbor (binary) indication vectors along with the anchor feature maps. The approximation error of such an anchored feature mapping approach is analyzed. We evaluate the performance of our approach on large-scale nonlinear support vector machines (SVMs) learning tasks in the context of visual object classification. Experimental results on several benchmark data sets show the superiority of our method over existing feature mapping methods in achieving reasonable trade-off between training time and testing accuracy.