mtybilly/Instruct2DS huggingface dataset:
Sparsifying Neural Network Connections for Face Recognition
Authors: Yi Sun, Xiaogang Wang, Xiaoou Tang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure 26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-the-art DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.
Pairwise Linear Regression Classification for Image Set Retrieval
Authors: Qingxiang Feng, Yicong Zhou, Rushi Lan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and the unrelated image set, we introduce a combination metric for two new classifiers based on two constitution strategies of the unrelated subspace. Extensive experiments on six well-known databases prove that the performance of PLRC is better than that of DLRC and several state-of-the-art classifiers for different vision recognition tasks: cluster-based face recognition, video-based face recognition, object recognition and action recognition.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Authors: Ira Kemelmacher-Shlizerman, Steven M. Seitz, Daniel Miller, Evan Brossard
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a person's age, and compare as a function of training data size (#photos and  #people).  We report results of state of the art  and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations at http://megaface.cs.washington.edu.
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition
Authors: Yandong Wen, Zhifeng Li, Yu Qiao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of AIFR arises from the fact that the facial appearance is subject to significant intra-personal changes caused by the aging process over time. In order to address this problem, we propose a novel deep face recognition framework to learn the age-invariant deep face features through a carefully designed CNN model. To the best of our knowledge, this is the first attempt to show the effectiveness of deep CNNs in advancing the state-of-the-art of AIFR. Extensive experiments are conducted on several public domain face aging datasets (MORPH Album2, FGNET, and CACD-VS) to demonstrate the effectiveness of the proposed model over the state-of-the-art. We also verify the excellent generalization of our new model on the famous LFW dataset.
Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity
Authors: Robert Walecki, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is  in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of target data. For this reason, majority of the methods proposed resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-of-the-art approach for the target task.
A Robust Multilinear Model Learning Framework for 3D Faces
Authors: Timo Bolkart, Stefanie Wuhrer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression. Existing methods to learn a multilinear face model degrade if not every person is captured in every expression, if face scans are noisy or partially occluded, if expressions are erroneously labeled, or if the vertex correspondence is inaccurate. These limitations impose requirements on the training data that disqualify large amounts of available 3D face data from being usable to learn a multilinear model. To overcome this, we introduce the first framework to robustly learn a multilinear model from 3D face databases with missing data, corrupt data, wrong semantic correspondence, and inaccurate vertex correspondence. To achieve this robustness to erroneous training data, our framework jointly learns a multilinear model and fixes the data. We evaluate our framework on two publicly available 3D face databases, and show that our framework achieves a data completion accuracy that is comparable to state-of-the-art tensor completion methods. Our method reconstructs corrupt data more accurately than state-of-the-art methods, and improves the quality of the learned model significantly for erroneously labeled expressions.
Ordinal Regression With Multiple Output CNN for Age Estimation
Authors: Zhenxing Niu, Mo Zhou, Le Wang, Xinbo Gao, Gang Hua
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem. However, the processes of extracting features and learning a regression model are often separated and optimized independently in previous work. In this paper, we propose an End-to-End learning approach to address ordinal regression problems using deep Convolutional Neural Network, which could simultaneously conduct feature learning and regression modeling. In particular, an ordinal regression problem is transformed into a series of binary classification sub-problems. And we propose a multiple output CNN learning algorithm to collectively solve these classification sub-problems, so that the correlation between these tasks could be explored. In addition, we publish an Asian Face Age Dataset (AFAD) containing more than 160K facial images with precise age ground-truths, which is the largest public age dataset to date. To the best of our knowledge, this is the first work to address ordinal regression problems by using CNN, and achieves the state-of-the-art performance on both the MORPH and AFAD datasets.
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Authors: Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter V. Gehler, Bernt Schiele
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation.
Thin-Slicing for Pose: Learning to Understand Pose Without Explicit Pose Estimation
Authors: Suha Kwak, Minsu Cho, Ivan Laptev
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures  differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.
A Dual-Source Approach for 3D Pose Estimation From a Single Image
Authors: Hashim Yasin, Umar Iqbal, Bjorn Kruger, Andreas Weber, Juergen Gall
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results and is even competitive when the skeleton structure of the two sources differ substantially.
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
Authors: Markus Oberweger, Gernot Riegler, Paul Wohlhart, Vincent Lepetit
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far. As a result, existing datasets are limited to a few sequences and individuals, with limited accuracy, and this prevents these methods from delivering their full potential. We propose a semi-automated method for efficiently and accurately labeling each frame of a hand depth video with the corresponding 3D locations of the joints: The user is asked to provide only an estimate of the 2D reprojections of the visible joints in some reference frames, which are automatically selected to minimize the labeling work by efficiently optimizing a sub-modular loss function. We then exploit spatial, temporal, and appearance constraints to retrieve the full 3D poses of the hand over the complete sequence. We show that this data can be used to train a recent state-of-the-art hand pose estimation method, leading to increased accuracy.
Sparseness Meets Deepness: 3D Human Pose Estimation From Monocular Video
Authors: Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos, Konstantinos G. Derpanis, Kostas Daniilidis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown. In the former case, a novel approach is introduced that integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the latter case, the former case is extended by treating the image locations of the joints as latent variables in order to take into account considerable uncertainties in 2D joint locations. A deep fully convolutional network is trained to predict the uncertainty maps of the 2D joint locations. The 3D pose estimates are realized via an Expectation-Maximization algorithm over the entire sequence, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Empirical evaluation on the Human3.6M dataset shows that the proposed approaches achieve greater 3D pose estimation accuracy over  state-of-the-art baselines.  Further, the proposed approach outperforms a publicly available 2D pose estimation baseline on the challenging PennAction dataset.
Answer-Type Prediction for Visual Question Answering
Authors: Kushal Kafle, Christopher Kanan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach's key insight is that we can predict the form of the answer from the question. We formulate our solution in a Bayesian framework. When our approach is combined with a discriminative model, the combined model achieves state-of-the-art results on four benchmark datasets for open-ended VQA: DAQUAR, COCO-QA, The VQA Dataset, and Visual7W.
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Authors: Satwik Kottur, Ramakrishna Vedantam, Jose M. F. Moura, Devi Parikh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a model to learn visually grounded word embeddings (vis-w2v) to capture visual notions of semantic relatedness. While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world. For instance, although "eats" and "stares at" seem unrelated in text, they share semantics visually. When people are eating something, they also tend to stare at the food. Grounding diverse relations like "eats" and "stares at" into vision remains challenging, despite recent progress in vision. We note that the visual grounding of words depends on semantics, and not the literal pixels. We thus use abstract scenes created from clipart to provide the visual grounding. We find that the embeddings we learn capture fine-grained, visually grounded notions of semantic relatedness. We show improvements over text-only word embeddings (word2vec) on three tasks: common-sense assertion classification, visual paraphrasing and text-based image retrieval. Our code and datasets are available online.
Visual7W: Grounded Question Answering in Images
Authors: Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.
Learning Deep Structure-Preserving Image-Text Embeddings
Authors: Liwei Wang, Yin Li, Svetlana Lazebnik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and text-to-image retrieval. Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase localization on the Flickr30K Entities dataset.
Yin and Yang: Balancing and Answering Binary Visual Questions
Authors: Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, Devi Parikh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The complex compositional structure of language makes problems at the intersection of vision and language challenging. But language also provides a strong prior that can result in good superficial performance, without the underlying models truly understanding the visual content.  This can hinder progress in pushing state of art in the computer vision aspects of multi-modal AI.  In this paper, we address binary Visual Question Answering (VQA) on abstract scenes. We formulate this problem as visual verification of concepts inquired in the questions. Specifically, we convert the question to a tuple that concisely summarizes the visual concept to be detected in the image. If the concept can be found in the image, the answer to the question is "yes", and otherwise "no". Abstract scenes play two roles (1) They allow us to focus on the high-level semantics of the VQA task as opposed to the low-level recognition problems, and perhaps more importantly, (2) They provide us the modality to balance the dataset such that language priors are controlled, and the role of vision is essential. In particular, we collect fine-grained pairs of scenes for every question, such that the answer to the question is "yes" for one scene, and "no" for the other for the exact same question. Indeed, language priors alone do not perform better than chance on our balanced dataset. Moreover, our proposed approach matches the performance of a state-of-the-art VQA approach on the unbalanced dataset, and outperforms it on the balanced dataset.
GIFT: A Real-Time and Scalable 3D Shape Search Engine
Authors: Song Bai, Xiang Bai, Zhichao Zhou, Zhaoxiang Zhang, Longin Jan Latecki
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines.  In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration; (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching; (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions
Functional Faces: Groupwise Dense Correspondence Using Functional Maps
Authors: Chao Zhang, William A. P. Smith, Arnaud Dessein, Nick Pears, Hang Dai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps. The functional maps paradigm brings with it a number of advantages for face correspondence. First, it allows us to combine various notions of correspondence. We do so by proposing a number of face-specific functions, suited to either within- or between-subject correspondence. Second, we propose a groupwise variant of the method allowing us to compute cycle-consistent functional maps between all faces in a training set. Since functional maps are of much lower dimension than point-to-point correspondences, this is feasible even when the input meshes are very high resolution. Finally, we show how a functional map provides a geometric constraint that can be used to filter feature matches between non-rigidly deforming surfaces.
Similarity Metric For Curved Shapes In Euclidean Space
Authors: Girum G. Demisse, Djamila Aouada, Bjorn Ottersten
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we introduce a similarity metric for curved shapes that can be described, distinctively, by ordered points. The proposed method represents a given curve as a point in the deformation space, the direct product of rigid transformation matrices, such that the  successive action of the matrices on a fixed starting point reconstructs the full curve. In general, both open and closed curves are represented in the deformation space modulo shape orientation and orientation preserving diffeomorphisms. The use of direct product Lie groups to represent curved shapes led to an explicit formula for geodesic curves and the formulation of a similarity metric between shapes by the L2-norm on the Lie algebra. Additionally, invariance to reparametrization or estimation of point correspondence between shapes is performed as an intermediate step for computing geodesics. Furthermore, since there is no computation of differential quantities on the curves, our representation is more robust to local perturbations and needs no pre-smoothing. We compare our method with the elastic shape metric defined through the square root velocity (SRV) mapping, and other shape matching approaches.
Shape Analysis With Hyperbolic Wasserstein Distance
Authors: Jie Shi, Wen Zhang, Yalin Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Shape space is an active research field in computer vision study. The shape distance defined in a shape space may provide a simple and refined index to represent a unique shape. Wasserstein distance defines a Riemannian metric for the Wasserstein space. It intrinsically measures the similarities between shapes and is robust to image noise. Thus it has the potential for the 3D shape indexing and classification research. While the algorithms for computing Wasserstein distance have been extensively studied, most of them only work for genus-0 surfaces. This paper proposes a novel framework to compute Wasserstein distance between general topological surfaces with hyperbolic metric. The computational algorithms are based on Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram and the method is general and robust. We apply our method to study human facial expression, longitudinal brain cortical morphometry with normal aging, and cortical shape classification in Alzheimer's disease (AD). Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.
Tensor Power Iteration for Multi-Graph Matching
Authors: Xinchu Shi, Haibin Ling, Weiming Hu, Junliang Xing, Yanning Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic computational intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor approximation framework. In particular, we formulate multi-graph matching as a combinational optimization problem with two main ingredients: unary matching over graph vertices and structure matching over graph edges, both of which across multiple graphs. Then we propose an efficient power iteration solution for the resulted NP-hard optimization problem. The proposed algorithm has several advantages: 1) the intrinsic matching consistency across multiple graphs based on the high-order tensor optimization; 2) the free employment of powerful high-order node affinity; 3) the flexible integration between various types of node affinities and edge/hyper-edge affinities. Experiments on diverse and challenging datasets validate the effectiveness of the proposed approach in comparison with state-of-the-arts.
Multivariate Regression on the Grassmannian for Predicting Novel Domains
Authors: Yongxin Yang, Timothy M. Hospedales
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We study the problem of predicting how to recognise visual objects in novel domains with neither labelled nor unlabelled training data. Domain adaptation is now an established research area due to its value in ameliorating the issue of domain shift between train and test data. However, it is conventionally assumed that domains are discrete entities, and that at least unlabelled data is provided in testing domains. In this paper, we consider the case where domains are parametrised by a vector of continuous values (e.g., time, lighting or view angle). We aim to use such domain metadata to predict novel domains for recognition. This allows a recognition model to be pre-calibrated for a new domain in advance (e.g., future time or view angle) without waiting for data collection and re-training. We achieve this by posing the problem as one of multivariate regression on the Grassmannian, where we regress a domain's subspace (point on the Grassmannian) against an independent vector of domain parameters. We derive two novel methodologies to achieve this challenging task: a direct kernel regression, and an indirect method with better extrapolation properties. We evaluate our methods on two cross-domain visual recognition benchmarks, where they perform close to the upper bound of full data domain adaptation. This demonstrates that data is not necessary for domain adaptation if a domain can be parametrically described.
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation
Authors: Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified.
Geospatial Correspondences for Multimodal Registration
Authors: Diego Marcos, Raffay Hamid, Devis Tuia
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across the world. To do so, images of the same geographical areas acquired at different times and, potentially, with different sensors must be efficiently parsed to update maps and detect land-cover changes. However, a naive transfer of ground truth labels from one location in the source image to the corresponding location in the target image is not generally feasible, as these images are often only loosely registered (with up to +- 50m of non-uniform errors). Furthermore, land-cover changes in an area over time must be taken into account for an accurate ground truth transfer. To tackle these challenges, we propose a mid-level sensor-invariant representation that encodes image regions in terms of the spatial distribution of their spectral neighbors. We incorporate this representation in a Markov Random Field to simultaneously account for nonlinear mis-registrations and enforce locality priors to find matches between multi-sensor images. We show how our approach can be used to assist in several multimodal land-cover update and change detection problems.
Constrained Deep Transfer Feature Learning and Its Applications
Authors: Yue Wu, Qiang Ji
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however,typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot transfer learning and often ignore the specific properties that the transferred data must satisfy. To address these issues, we introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning by performing transfer learning in a progressively improving feature space iteratively in order to better narrow the gap between the target domain and the source domain for effective transfer of the data from source domain to target domain. Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain.  To demonstrate the effectiveness of the proposed constrained deep transfer feature learning method, we apply it to thermal feature learning for eye detection by transferring from the visible domain. We also applied the proposed method for cross-view facial expression recognition as a second application. The experimental results demonstrate the effectiveness of the proposed method for both applications.
Deep Canonical Time Warping
Authors: George Trigeorgis, Mihalis A. Nicolaou, Stefanos Zafeiriou, Bjorn W. Schuller
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Machine learning algorithms for the analysis of time-series often depend on the assumption that the utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture the properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards the temporal alignment of time-series are applied directly on the observation space, or utilise simple linear projections.  Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e.g., aligning visual and acoustic information). To this end, we present the Deep Canonical Time Warping (DCTW), a method which automatically learns complex non-linear representations of multiple time-series, generated such that (i) they are highly correlated, and (ii) temporally in alignment. By means of experiments on four real datasets, we show that the representations learnt via the proposed DCTW significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with highly heterogeneous features, such as the temporal alignment of acoustic and visual features.
Multilinear Hyperplane Hashing
Authors: Xianglong Liu, Xinjie Fan, Cheng Deng, Zhujin Li, Hao Su, Dacheng Tao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Hashing has become an increasingly popular technique for fast nearest neighbor search in large databases. Despite its successful progress in classic point-to-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up in many applications like active learning with SVMs. Existing hyperplane hashing methods enable the fast search based on the randomly generated hash codes, but still suffer from a low collision probability and thus usually require long codes for a satisfying performance. To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. Our theoretical analysis shows that as a product of an even number of random linear projections, the multilinear hash function possesses an increasing power of locality sensitivity to the hyperplane queries. To leverage its sensitivity to the angle distance, we further introduce an angular quantization based learning framework for compact multilinear hashing, which considerably boosts the search performance with less hash bits. Experiments with applications to large-scale (up to one million) active learning on two datasets demonstrate the overall superiority of the proposed approach.
Large Scale Hard Sample Mining With Monte Carlo Tree Search
Authors: Olivier Canevet, Francois Fleuret
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We investigate an efficient strategy to collect false positives from very large training sets in the context of object detection. Our approach scales up the standard bootstrapping procedure by using a hierarchical decomposition of an image collection which reflects the statistical regularity of the detector's responses.  Based on that decomposition, our procedure uses a Monte Carlo Tree Search to prioritize the sampling toward sub-families of images which have been observed to be rich in false positives, while maintaining a fraction of the sampling toward unexplored sub-families of images. The resulting procedure increases substantially the proportion of false positive samples among the visited ones compared to a naive uniform sampling.  We apply experimentally this new procedure to face detection with a collection of 100,000 background images and to pedestrian detection with 32,000 images. We show that for two standard detectors, the proposed strategy cuts the number of images to visit by half to obtain the same amount of false positives and the same final performance.
Multi-Label Ranking From Positive and Unlabeled Data
Authors: Atsushi Kanehira, Tatsuya Harada
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we specifically examine the training of a multi-label classifier from data with incompletely assigned labels. This problem is fundamentally important in many multi-label applications because it is almost impossible for human annotators to assign a complete set of labels, although their judgments are reliable. In other words, a multi-label dataset usually has properties by which (1) assigned labels are definitely positive and (2) some labels are absent but are still considered positive. Such a setting has been studied as a positive and unlabeled (PU) classification problem in a binary setting. We treat incomplete label assignment problems as a multi-label PU ranking, which is an extension of classical binary PU problems to the well-studied rank-based multi-label classification. We derive the conditions that should be satisfied to cancel the negative effects of label incompleteness. Our experimentally obtained results demonstrate the effectiveness of these conditions.
Joint Unsupervised Learning of Deep Representations and Image Clusters
Authors: Jianwei Yang, Devi Parikh, Dhruv Batra
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we propose a recurrent framework for joint unsupervised learning of deep representations and image clusters. In our framework, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Convolutional Neural Network (CNN). During training, image clusters and representations are updated jointly: image clustering is conducted in the forward pass, while representation learning in the backward pass. Our key idea behind this framework is that good representations are beneficial to image clustering and clustering results provide supervisory signals to representation learning. By integrating two processes into a single model with a unified weighted triplet loss function and optimizing it end-to-end, we can obtain not only more powerful representations, but also more precise image clusters. Extensive experiments show that our method outperforms the state-of-the-art on image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to other tasks.
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Authors: Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks. However, SSC applies only to vector data in Euclidean space. As such, there is still no satisfactory approach to solve  subspace clustering by self-expressive principle for symmetric positive definite(SPD) matrices which is very useful in computer vision. In this paper, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), a kernel subspace clustering method is constructed on the SPD manifold through an appropriate Log-Euclidean kernel, termed as kernel sparse subspace clustering on the SPD Riemannian manifold  (KSSCR). By exploiting the intrinsic Riemannian Geometry within data, KSSCR can effectively characterize the geodesic distance between SPD matrices to uncover the underlying subspace structure. Experimental results on several famous database demonstrate that the proposed method achieves better clustering results than the state-of-the-art approaches.
Symmetry reCAPTCHA
Authors: Chris Funk, Yanxi Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This is a reaction to the poor performance of symmetry detection algorithms on real-world images, benchmarked since CVPR 2011. Our systematic study reveals significant difference between human labeled (reflection and rotation) symmetries on photos and the output of computer vision algorithms on the same photo set. We exploit this human-machine symmetry perception gap by proposing a novel symmetry-based Turing test. By leveraging a comprehensive user interface, we collected more than 78,000 symmetry labels from 400 Amazon Mechanical Turk raters on nearly 1,000 photos from the Microsoft COCO dataset. Using a set of ground-truth symmetries automatically generated from noisy human labels, the effectiveness of our work is evidenced by a separate test where over 96% success rate is achieved. We demonstrate statistically significant outcomes for using symmetry perception as a powerful, alternative, image-based reCAPTCHA.
Unsupervised Learning of Discriminative Attributes and Visual Representations
Authors: Chen Huang, Chen Change Loy, Xiaoou Tang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.
When VLAD Met Hilbert
Authors: Mehrtash Harandi, Mathieu Salzmann, Fatih Porikli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In many challenging visual recognition tasks where training data is limited,  Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-of-the-art approaches. In this paper, we address two fundamental limitations of VLAD:  its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate non-vector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks.  Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
Authors: Ha Quang Minh, Marco San Biagio, Loris Bazzani, Vittorio Murino
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides a rich representation of image features by exploiting their non-linear correlations, using the power of kernel methods and Riemannian geometry. Theoretically, we provide an approximate formulation for the Log-Hilbert-Schmidt distance between covariance operators that is efficient to compute and scalable to large datasets. Empirically, we apply our framework to the task of image classification on eight different, challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.