mtybilly/Instruct2DS huggingface dataset:
Adaptive Hashing for Fast Similarity Search
Authors: Fatih Cakir, Stan Sclaroff
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: With the staggering growth in image and video datasets, algorithms that provide fast similarity search and compact storage are crucial. Hashing methods that map the data into Hamming space have shown promise; however, many of these methods employ a batch-learning strategy in which the computational cost and memory requirements may become intractable and infeasible with larger and larger datasets. To overcome these challenges, we propose an online learning algorithm based on stochastic gradient descent in which the hash functions are updated iteratively with streaming data. In experiments with three image retrieval benchmarks, our online algorithm attains retrieval accuracy that is comparable to competing state-of-the-art batch-learning solutions, while our formulation is orders of magnitude faster and being online it is adaptable to the variations of the data. Moreover, our formulation yields improved retrieval performance over a recently reported online hashing technique, Online Kernel Hashing.
Single Image 3D Without a Single 3D Image
Authors: David F. Fouhey, Wajahat Hussain, Abhinav Gupta, Martial Hebert
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Do we really need 3D labels in order to learn how to predict 3D? In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label. Rather than use explicit supervision, we use the regularity of indoor scenes to learn the mapping in a completely unsupervised manner. We demonstrate this on both a standard 3D scene understanding dataset as well as Internet images for which 3D is unavailable, precluding supervised learning. Despite never seeing a 3D label, our method produces competitive results.
Cross-Domain Image Retrieval With a Dual Attribute-Aware Ranking Network
Authors: Junshi Huang, Rogerio S. Feris, Qiang Chen, Shuicheng Yan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting/pose/background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two subnetworks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).
Attribute-Graph: A Graph Based Approach to Image Ranking
Authors: Nikita Prabhu, R. Venkatesh Babu
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a novel image representation, termed Attribute-Graph, to rank images by their semantic similarity to a given query image. An Attribute-Graph is an undirected fully connected graph, incorporating both local and global image characteristics. The graph nodes characterise objects as well as the overall scene context using mid-level semantic attributes, while the edges capture the object topology. We demonstrate the effectiveness of Attribute-Graphs by applying them to the problem of image ranking. We benchmark the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets, which we have created in order to evaluate the ranking performance on complex queries containing multiple objects. Our experimental evaluation shows that modelling images as Attribute-Graphs results in improved ranking performance over existing techniques.
Contextual Action Recognition With R*CNN
Authors: Georgia Gkioxari, Ross Girshick, Jitendra Malik
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: There are multiple cues in an image which reveal what action a person is performing. For example, a jogger has a pose that is characteristic for jogging, but the scene (e.g. road, trail) and the presence of other joggers can be an additional source of information. In this work, we exploit the simple observation that actions are accompanied by contextual cues to build a strong action recognition system. We adapt RCNN to use more than one region for classification while still maintaining the ability to localize the action. We call our system R*CNN. The action-specific models and the feature maps are trained jointly, allowing for action specific representations to emerge. R*CNN achieves 90.2% mean AP on the PASAL VOC Action dataset, outperforming all other approaches in the field by a significant margin. Last, we show that R*CNN is not limited to action recognition. In particular, R*CNN can also be used to tackle fine-grained tasks such as attribute classification. We validate this claim by reporting state-of-the-art performance on the Berkeley Attributes of People dataset.
What Makes an Object Memorable?
Authors: Rachit Dubey, Joshua Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Recent studies on image memorability have shed light on what distinguishes the memorability of different images and the intrinsic and extrinsic properties that make those images memorable. However, a clear understanding of the memorability of specific objects inside an image remains elusive. In this paper, we provide the first attempt to answer the question: what exactly is remembered about an image? We augment both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors and properties that make an object memorable (or forgettable) to humans. We analyze various visual factors that may influence object memorability (e.g. color, visual saliency, and object categories). We also study the correlation between object and image memorability and find that image memorability is greatly affected by the memorability of its most memorable object. Lastly, we explore the effectiveness of deep learning and other computational approaches in predicting object memorability in images. Our efforts offer a deeper understanding of memorability in general thereby opening up avenues for a wide variety of applications.
kNN Hashing With Factorized Neighborhood Representation
Authors: Kun Ding, Chunlei Huo, Bin Fan, Chunhong Pan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Hashing is very effective for many tasks in reducing the processing time and in compressing massive databases. Although lots of approaches have been developed to learn data-dependent hash functions in recent years, how to learn hash functions to yield good performance with acceptable computational and memory cost is still a challenging problem. Based on the observation that retrieval precision is highly related to the kNN classification accuracy, this paper proposes a novel kNN-based supervised hashing method, which learns hash functions by directly maximizing the kNN accuracy of the Hamming-embedded training data. To make it scalable well to large problem, we propose a factorized neighborhood representation to parsimoniously model the neighborhood relationships inherent in training data. Considering that real-world data are often linearly inseparable, we further kernelize this basic model to improve its performance. As a result, the proposed method is able to learn accurate hashing functions with tolerable computation and storage cost. Experiments on four benchmarks demonstrate that our method outperforms the state-of-the-arts.
Multi-View Complementary Hash Tables for Nearest Neighbor Search
Authors: Xianglong Liu, Lei Huang, Cheng Deng, Jiwen Lu, Bo Lang
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Recent years have witnessed the success of hashing techniques in fast nearest neighbor search. In practice many applications (e.g., visual search, object detection, image matching, etc.) have enjoyed the benefits of complementary hash tables and information fusion over multiple views. However, most of prior research mainly focused on compact hash code cleaning, and rare work studies how to build multiple complementary hash tables, much less to adaptively integrate information stemming from multiple views. In this paper we first present a novel multi-view complementary hash table method that learns complementarity hash tables from the data with multiple views. For single multi-view table, using exemplar based feature fusion, we approximate the inherent data similarities with a low-rank matrix, and learn discriminative hash functions in an efficient way. To build complementary tables and meanwhile maintain scalable training and fast out-of-sample extension, an exemplar reweighting scheme is introduced to update the induced low-rank similarity in the sequential table construction framework, which indeed brings mutual benefits between tables by placing greater importance on exemplars shared by mis-separated neighbors. Extensive experiments on three large-scale image datasets demonstrate that the proposed method significantly outperforms various naive solutions and state-of-the-art multi-table methods.
Scalable Person Re-Identification: A Benchmark
Authors: Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale; 2) consist of hand-drawn bboxes, which are unavailable under realistic settings; 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera.  As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition
Authors: Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Most of the feature-learning methods for RGB-D object recognition either learn features from color and depth modalities separately, or simply treat RGB-D as undifferentiated four-channel data, which cannot adequately exploit the relationship between different modalities. Motivated by the intuition that different modalities should contain not only some modal-specific patterns but also some shared common patterns, we propose a multi-modal feature learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities. In this way, we obtain features reflecting shared properties as well as modal-specific properties in different modalities. The information of the multi-modal learning frameworks is back-propagated to the early CNN layers. Experimental results show that our proposed multi-modal feature learning method outperforms state-of-the-art approaches on two widely used RGB-D object benchmark datasets.
Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model
Authors: Spyros Gidaris, Nikos Komodakis
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.
Neural Activation Constellations: Unsupervised Part Model Discovery With Convolutional Networks
Authors: Marcel Simon, Erik Rodner
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object. We present an approach that is able to learn part models in a  completely unsupervised manner, without part annotations and even without given bounding boxes during learning. The key idea is to find constellations of neural activation patterns computed using convolutional neural networks. In our experiments, we outperform existing approaches for fine-grained recognition on the CUB200-2011, Oxford PETS, and Oxford Flowers dataset in case no part or bounding box annotations are available and achieve state-of-the-art performance for the Stanford Dog dataset. We also show the benefits of neural constellation models as a data augmentation technique for fine-tuning. Furthermore, our paper unites the areas of generic and fine-grained classification, since our approach is suitable for both scenarios.
Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection
Authors: David Novotny, Jiri Matas
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: A novel efficient method for extraction of object proposals is introduced. Its "objectness" function exploits deep spatial pyramid features, a novel fast-to-compute HoG-based edge statistic  and the EdgeBoxes score. The efficiency is achieved by the use of spatial bins in a novel combination with sparsity-inducing group normalized SVM.  State-of-the-art recall performance is achieved on Pascal VOC07, significantly outperforming methods with comparable speed. Interestingly, when only 100 proposals per image are considered the method attains 78 % recall on VOC07.  The method improves mAP of the RCNN class-specific detector, increasing it by 10 points when only 50 proposals are used in each image. The system trained on twenty classes performs well on the two hundred class ILSVRC2013 set confirming generalization capability.
Probabilistic Label Relation Graphs With Ising Models
Authors: Nan Ding, Jia Deng, Kevin P. Murphy, Hartmut Neven
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.
Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD
Authors: Hyo Jin Kim, Enrique Dunn, Jan-Michael Frahm
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale. In particular, we discover features that are useful for recognizing a place in a data-driven manner, and use this knowledge to predict useful features in a query image prior to the geo-localization process. This allows us to achieve better performance while reducing the number of features. Also, for both learning to predict features and retrieving geo-tagged images from the database, we propose per-bundle vector of locally aggregated descriptors (PBVLAD), where each maximally stable region is described by a vector of locally aggregated descriptors (VLAD) on multiple scale-invariant features detected within the region. Experimental results show the proposed approach achieves a significant improvement over other baseline methods.
Task-Driven Feature Pooling for Image Classification
Authors: Guo-Sen Xie, Xu-Yao Zhang, Xiangbo Shu, Shuicheng Yan, Cheng-Lin Liu
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Feature pooling is an important strategy to achieve high performance in image classification. However, most pooling methods are unsupervised and heuristic. In this paper, we propose a novel  task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner. Different from the traditional methods (e.g., average and max pooling), TDP is an implicit pooling method which elegantly integrates the learning of representations into the given classification task. The optimization of TDP can equalize the similarities between the descriptors and the learned representation, and maximize the classification accuracy. TDP can be combined with the traditional BoW models (coding vectors) or the recent state-of-the-art CNN models (feature maps) to achieve a much better pooled representation. Furthermore, a self-training mechanism is used to generate the TDP representation for a new test image. A multi-task extension of TDP is also proposed to further improve the performance. Experiments on three databases (Flower-17, Indoor-67 and Caltech-101) well validate the effectiveness of our models.
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing
Authors: Sarah Taghavi Namin, Mohammad Najafi, Mathieu Salzmann, Lars Petersson
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the single modality scenario. Existing methods, however, assume that corresponding regions in two modalities have the same label. In this paper, we address the problem of data misalignment and label inconsistencies, e.g., due to moving objects, in semantic labeling, which violate the assumption of existing techniques. To this end, we formulate multimodal semantic labeling as inference in a CRF, and introduce latent nodes to explicitly model inconsistencies between two domains. These latent nodes allow us not only to leverage information from both domains to improve their labeling, but also to cut the edges between inconsistent regions. To eliminate the need for hand tuning the parameters of our model, we propose to learn intra-domain and inter-domain potential functions from training data. We demonstrate the benefits of our approach on two publicly available datasets containing 2D imagery and 3D point clouds. Thanks to our latent nodes and our learning strategy, our method outperforms the state-of-the-art in both cases.
One Shot Learning via Compositions of Meaningful Patches
Authors: Alex Wong, Alan L. Yuille
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: The task of discriminating one object from another is almost trivial for a human being. However, this task is computationally taxing for most modern machine learning methods; whereas, we perform this task at ease given very few examples for learning. It has been proposed that the quick grasp of concept may come from the shared knowledge between the new example and examples previously learned. We believe that the key to one-shot learning is the sharing of common parts as each part holds immense amounts of information on how a visual concept is constructed. We propose an unsupervised method for learning a compact dictionary of image patches representing meaningful components of an objects. Using those patches as features, we build a compositional model that outperforms a number of popular algorithms on a one-shot learning task. We demonstrate the effectiveness of this approach on hand-written digits and show that this model generalizes to multiple datasets.
FASText: Efficient Unconstrained Scene Text Detector
Authors: Michal Busta, Lukas Neumann, Jiri Matas
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a novel easy-to-implement stroke detector based on an efficient pixel intensity comparison to surrounding pixels. Stroke-specific keypoints are efficiently detected and text fragments are subsequently extracted by local thresholding guided by keypoint properties. Classification based on effectively calculated features then eliminates non-text regions.  The stroke-specific keypoints produce 2 times less region segmentations and still detect 25% more characters than the commonly exploited MSER detector and the process is 4 times faster. After a novel efficient classification step, the number of regions is reduced to 7 times less than the standard method and is still almost 3 times faster.  All stages of the proposed pipeline are scale- and rotation-invariant and support a wide variety of scripts (Latin, Hebrew, Chinese, etc.) and fonts. When the proposed detector is plugged into a scene text localization and recognition pipeline, a state-of-the-art text localization accuracy is maintained  whilst the processing time is significantly reduced.
Multi-Scale Recognition With DAG-CNNs
Authors: Songfan Yang, Deva Ramanan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multi-scale features that can be effectively shared between coarse and fine-grained classification tasks. While fine-tuning such models helps performance, we show that even "off-the-self" multi-scale features perform quite well. We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15). In terms of the heavily benchmarked MIT67 and Scene15 datasets, our results reduce the lowest previously-reported error by 23.9% and 9.5%, respectively.
Relaxed Multiple-Instance SVM With Application to Object Discovery
Authors: Xinggang Wang, Zhuotun Zhu, Cong Yao, Xiang Bai
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Multiple-instance learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking. In this paper, we propose a novel method to solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM). We treat the positiveness of instance as a continuous variable, use Noisy-OR model to enforce the MIL constraints, and optimize them jointly in a unified framework. The optimization problem can be efficiently solved using stochastic gradient decent. The extensive experiments demonstrate that RMI-SVM consistently achieves superior performance on various benchmarks for MIL. Moreover, we simply applied RMI-SVM to a challenging vision task, common object discovery. The state-of-the arts results of object discovery on PASCAL VOC datasets further confirm the advantages of the proposed method.
Im2Calories: Towards an Automated Mobile Vision Food Diary
Authors: Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the classifier (running on your phone) to predict which foods are present in your meal, and we lookup the corresponding nutritional facts. We apply this method to a new dataset of images from 23 different restaurants, using a CNN-based classifier, significantly outperforming previous work. The more challenging setting works outside of restaurants. In this case, we need to estimate the size of the foods, as well as their labels. This requires solving segmentation and depth / volume estimation from a single image. We present CNN-based approaches to these problems, with promising preliminary results.
LEWIS: Latent Embeddings for Word Images and their Semantics
Authors: Albert Gordo, Jon Almazan, Naila Murray, Florent Perronin
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images. Although text recognition and retrieval have received a lot of attention in recent years, previous works have focused on recognizing or retrieving exactly the same word used as a query, without taking the                                                                                   In this paper, we ask the following question: can we predict semantic concepts directly from a word image, without explicitly trying to transcribe the word image or its characters at any point? For this goal we propose a convolutional neural network (CNN) with a weighted ranking loss objective that ensures that the concepts relevant to the query image are ranked ahead of those that are not relevant. This can also be interpreted as learning a Euclidean space where word images and concepts are jointly embedded. This model is learned in an end-to-end manner, from image pixels to semantic concepts, using a dataset of synthetically generated word images and concepts mined from a lexical database (WordNet). Our results show that, despite the complexity of the task, word images and concepts can indeed be associated with a high degree of accuracy.
Per-Sample Kernel Adaptation for Visual Recognition and Grouping
Authors: Borislav Antic, Bjorn Ommer
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Object, action, or scene representations that are corrupted by noise significantly impair the performance of visual recognition. Typically, partial occlusion, clutter, or excessive articulation affects only a subset of all feature dimensions and, most importantly, different dimensions are corrupted in different samples. Nevertheless, the common approach to this problem in feature selection and kernel methods is to down-weight or eliminate entire training samples or the same dimensions of all samples. Thus, valuable signal is lost, resulting in suboptimal classification.  Our goal is, therefore, to adjust the contribution of individual feature dimensions when comparing any two samples and computing their similarity. Consequently, per-sample selection of informative dimensions is directly integrated into kernel computation. The interrelated problems of learning the parameters of a kernel classifier and determining the informative components of each sample are then addressed in a joint objective function.  The approach can be integrated into the learning stage of any kernel-based visual recognition problem and it does not affect the computational performance in the retrieval phase. Experiments on diverse challenges of action recognition in videos and indoor scene classification show the general applicability of the approach and its ability to improve learning of visual representations.
Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations
Authors: Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, Jizhou Sun
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Detecting fine-grained subtle changes among a scene is critically important in practice. Previous change detection methods, focusing on detecting large-scale significant changes, cannot do this well. This paper proposes a feasible end-to-end approach to this challenging problem. We start from active camera relocation that quickly relocates camera to nearly the same pose and position of the last time observation. To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions. Given two times observations, we formulate fine-grained change detection as a joint optimization problem of three related factors, i.e., normal-aware lighting difference, camera geometry correction flow, and real scene change mask. We solve the three factors in a coarse-to-fine manner and achieve reliable change decision by rank minimization. We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions. Extensive experiments show the superior performance of our approach over state-of-the-art change detection methods and its ability to distinguish real scene changes from false ones caused by lighting variations.
Aggregating Local Deep Features for Image Retrieval
Authors: Artem Babenko, Victor Lempitsky
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor.  In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.
Learning Deep Object Detectors From 3D Models
Authors: Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Crowdsourced 3D CAD models are easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective, especially when real training data is limited or not well matched to the target domain. Most freely available CAD models capture 3D shape but are often missing other low level cues, such as realistic object texture, pose, or background. In a detailed analysis, we use synthetic CAD images to probe the ability of DCNN to learn without these cues, with surprising findings. In particular, we show that when the DCNN is fine-tuned on the target detection task, it exhibits a large degree of invariance to missing low-level cues, but, when pretrained on generic ImageNet classification, it learns better when the low-level cues are simulated. We show that our synthetic DCNN training approach significantly outperforms previous methods on the benchmark PASCAL VOC2007 dataset when learning in the few-shot scenario and improves performance in a domain shift scenario on the Office benchmark.
Harvesting Discriminative Meta Objects With Deep CNN Features for Scene Classification
Authors: Ruobing Wu, Baoyuan Wang, Wenping Wang, Yizhou Yu
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region proposal technique to generate a set of high-quality patches potentially containing objects, and apply a pre-trained CNN to extract generic deep features from these patches. Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. We further apply discriminative clustering enhanced with local CNN fine-tuning to aggregate similar objects and parts into groups, called meta objects. A scene image representation is constructed by pooling the feature response maps of all the learned meta objects at multiple spatial scales. We have confirmed that the scene image representation obtained using this new pipeline is capable of delivering state-of-the-art performance on two popular scene benchmark datasets, MIT Indoor 67 [22] and Sun397 [31].
Scalable Nonlinear Embeddings for Semantic Category-Based Image Retrieval
Authors: Gaurav Sharma, Bernt Schiele
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a novel algorithm for the task of supervised discriminative distance learning by nonlinearly embedding vectors into a low dimensional Euclidean space. We work in the challenging setting where supervision is with constraints on similar and dissimilar pairs while training. The proposed method is derived by an approximate kernelization of a linear Mahalanobis-like distance metric learning algorithm and can also be seen as a kernel neural network. The number of model parameters and test time evaluation complexity of the proposed method are O(dD) where D is the dimensionality of the input features and d is the dimension of the projection space -- this is in contrast to the usual kernelization methods as, unlike them, the complexity does not scale linearly with the number of training examples. We propose a stochastic gradient based learning algorithm which makes the method scalable (w.r.t. the number of training examples), while being nonlinear. We train the method with up to half a million training pairs of 4096 dimensional CNN features. We give empirical comparisons with relevant baselines on seven challenging datasets for the task of low dimensional semantic category based image retrieval.
Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis
Authors: Jorge Garcia, Niki Martinel, Christian Micheloni, Alfredo Gardel
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information.  Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos
Authors: Nima Sedaghat, Thomas Brox
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Object recognition approaches have recently been extended to yield, aside of the object class output, also viewpoint or pose. Training such approaches typically requires additional viewpoint or keypoint annotation in the training data or, alternatively, synthetic CAD models. In this paper,we present an approach that creates a dataset of images annotated with bounding boxes and viewpoint labels in a fully automated manner from videos. We assume that the scene is static in order to reconstruct 3D surfaces via structure from motion. We automatically detect when the reconstruction fails and normalize for the viewpoint of the 3D models by aligning the reconstructed point clouds. Exemplarily for cars we show that we can expand a large dataset of annotated single images and obtain improved performance when training a viewpoint regressor on this joined dataset.
Structured Indoor Modeling
Authors: Satoshi Ikehata, Hang Yang, Yasutaka Furukawa
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper presents a novel 3D modeling framework that reconstructs an indoor scene as a structured model from panorama RGBD images. A scene geometry is represented as a graph, where nodes correspond to structural elements such as rooms, walls, and objects. The approach devises a structure grammar that defines how a scene graph can be manipulated. The grammar then drives a principled new reconstruction algorithm, where the grammar rules are sequentially applied to recover a structured model.  The paper also proposes a new room segmentation algorithm and an offset-map reconstruction algorithm that are used in the framework and can enforce architectural shape priors far beyond existing state-of-the-art. The structured scene representation enables a variety of novel applications, ranging from indoor scene visualization, automated floorplan generation, Inverse-CAD, and more.  We have tested our framework and algorithms on six synthetic and five real datasets with qualitative and quantitative evaluations.
3D Time-Lapse Reconstruction From Internet Photos
Authors: Ricardo Martin-Brualla, David Gallup, Steven M. Seitz
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Given an Internet photo collection of a landmark, we compute a 3D time-lapse video sequence where a virtual camera moves continuously in time and space. While previous work assumed a static camera, the addition of camera motion during the time-lapse creates a very compelling impression of parallax.  Achieving this goal, however, requires addressing multiple technical challenges, including solving for time-varying depth maps, regularizing 3D point color profiles over time, and reconstructing high quality, hole-free images at every frame from the projected profiles. Our results show photorealistic time-lapses of skylines and natural scenes over many years, with dramatic parallax effects.
Global, Dense Multiscale Reconstruction for a Billion Points
Authors: Benjamin Ummenhofer, Thomas Brox
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with non-uniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently from the dual structure, and also memory efficiency is optimized, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.
On the Visibility of Point Clouds
Authors: Sagi Katz, Ayellet Tal
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Is it possible to determine the visible subset of points directly from a given point cloud? Interestingly, in [7] it was shown that this is indeed the case - despite the fact that points cannot occlude each other, this task can be performed without surface reconstruction or normal estimation. The operator is very simple - it first transforms the points to a new domain and then constructs the convex hull in that domain. Points that lie on the convex hull of the transformed set of points are the images of the visible points. This operator found numerous applications in computer vision, including face reconstruction, keypoint detection, finding the best viewpoints, reduction of points, and many more. The current paper addresses a fundamental question: What properties should a transformation function satisfy, in order to be utilized in this operator? We show that three such properties are sufficient: the sign of the function, monotonicity, and a condition regarding the function's parameter.  The correctness of an algorithm that satisfies these three properties is proved. Finally, we show an interesting application of the operator - assignment of visibility-confidence score. This feature is missing from previous approaches, where a binary yes/no visibility is determined. This score can be utilized in various applications; we illustrate its use in view-dependent curvature estimation.
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts
Authors: Niloufar Pourian, S. Karthikeyan, B.S. Manjunath
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a weakly-supervised approach to semantic segmentation. The goal is to assign pixel-level labels given only partial information, for example, image-level labels. This is an important problem in many application scenarios where it is difficult to get accurate segmentation or not feasible to obtain detailed annotations. The proposed approach starts with an initial coarse segmentation, followed by a spectral clustering approach that groups related image parts into communities. A community-driven graph is then constructed that captures spatial and feature relationships between communities while a label graph captures correlations between image labels. Finally, mapping the image level labels to appropriate communities is formulated as a convex optimization problem. The proposed approach does not require location information for image level labels and can be trained using partially labeled datasets. Compared to the state-of-the-art weakly supervised approaches, we achieve a significant performance improvement of 9% on MSRC-21 dataset and 11% on LabelMe dataset, while being more than 300 times faster.