pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Track: Online Multi-Object Tracking by Decision Making
Authors: Yu Xiang, Alexandre Alahi, Silvio Savarese
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Autonomous Driving, Decision Making, Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Online Multi-Object Tracking, Reinforcement Learning, Robot Navigation

Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving. In tracking-by-detection, a major challenge of online MOT is how to robustly associate noisy object detections on a new video frame with previously tracked objects. In this work, we formulate the online MOT problem as decision making in Markov Decision Processes (MDPs), where the lifetime of an object is modeled with a MDP. Learning a similarity function for data association is equivalent to learning a policy for the MDP, and the policy learning is approached in a reinforcement learning fashion which benefits from both advantages of offline-learning and online-learning for data association. Moreover, our framework can naturally handle the birth/death and appearance/disappearance of targets by treating them as state transitions in the MDP while leveraging existing online single object tracking methods. We conduct experiments on the MOT Benchmark to verify the effectiveness of our method.
Using Sparse Elimination for Solving Minimal Problems in Computer Vision
Authors: Janne Heikkila
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

Finding a closed form solution to a system of polynomial equations is a common problem in computer vision as well as in many other areas of engineering and science. Groebner basis techniques are often employed to provide the solution, but implementing an efficient Groebner basis solver to a given problem requires strong expertise in algebraic geometry. One can also convert the equations to a polynomial eigenvalue problem (PEP) and solve it using linear algebra, which is a more accessible approach for those who are not so familiar with algebraic geometry. In previous works PEP has been successfully applied for solving some relative pose problems in computer vision, but its wider exploitation is limited by the problem of finding a compact monomial basis. In this paper, we propose a new algorithm for selecting the basis that is in general more compact than the basis obtained with a state-of-the-art algorithm making PEP a more viable option for solving polynomial equations. Another contribution is that we present two minimal problems for camera self-calibration based on homography, and demonstrate experimentally using synthetic and real data that our algorithm can provide a numerically stable solution to the camera focal length from two homographies of unknown planar scene.
Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms
Authors: Henning Tjaden, Ulrich Schwanecke, Elmar Schomer
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Pose Estimation, Template Matching

We present a novel approach to 6DOF pose estimation and segmentation of rigid 3D objects using a single monocular RGB camera based on temporally consistent, local color histograms. We show that this approach outperforms previous methods in cases of cluttered backgrounds, heterogenous objects, and occlusions. The proposed histograms can be used as statistical object descriptors within a template matching strategy for pose recovery after temporary tracking loss e. g. caused by massive occlusion or if the object leaves the camera's field of view. The descriptors can be trained online within a couple of seconds moving a handheld object in front of a camera. During the training stage, our approach is already capable to recover from accidental tracking loss. We demonstrate the performance of our method in comparison to the state of the art in different challenging experiments including a popular public data set.
Colored Point Cloud Registration Revisited
Authors: Jaesik Park, Qian-Yi Zhou, Vladlen Koltun
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Point Cloud Registration

We present an algorithm for tightly aligning two colored point clouds. The key idea is to optimize a joint photometric and geometric objective that locks the alignment along both the normal direction and the tangent plane. We extend a photometric objective for aligning RGB-D images to point clouds, by locally parameterizing the point cloud with a virtual camera. Experiments demonstrate that our algorithm is more accurate and more robust than prior point cloud registration algorithms, including those that utilize color information. We use the presented algorithms to enhance a state-of-the-art scene reconstruction system. The accuracy of the resulting system is demonstrated on real-world scenes with accurate ground-truth models.
A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans
Authors: Rudrasis Chakraborty, Vikas Singh, Nagesh Adluru, Baba C. Vemuri
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

Analyzing data representing multifarious trajectories is central to the many fields in Science and Engineering; for example, trajectories representing a tennis serve, a gymnast's parallel bar routine, progression/remission of disease and so on. We present a novel geometric algorithm for performing statistical analysis of trajectories with distinct number of samples representing longitudinal (or temporal) data. A key feature of our proposal is that unlike existing schemes, our model is deployable in regimes where each participant provides a different number of acquisitions (trajectories have different number of sample points). To achieve this, we develop a novel method involving the parallel transport of the tangent vectors along each given trajectory to the starting point of the respective trajectories and then use the span of the matrix whose columns consist of these vectors, to construct a linear subspace in R^m. We then map these linear subspaces of R^m on to a single high dimensional hypersphere. This enables computing group statistics over trajectories by instead performing statistics on the hypersphere (equipped with a simpler geometry). Given a point on the hypersphere representing a trajectory, we also provide a "reverse mapping" algorithm to uniquely (under certain assumptions) reconstruct the subspace that corresponds to this point. Finally, by using existing algorithms for recursive Frechet mean and exact principal geodesic analysis on the hypersphere, we present several experiments on synthetic and real (vision and medical) data sets showing how group testing on such diversely sampled longitudinal data is possible by analyzing the reconstructed data in the subspace spanned by the first few PGs.
An Optimal Transportation Based Univariate Neuroimaging Index
Authors: Liang Mi, Wen Zhang, Junwei Zhang, Yonghui Fan, Dhruman Goradia, Kewei Chen, Eric M. Reiman, Xianfeng GU, Yalin Wang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

The alterations of brain structures and functions have been considered closely correlated to the change of cognitive performance due to neurodegenerative diseases such as Alzheimer's disease. In this paper, we introduce a variational framework to compute the optimal transformation (OT) in 3D space and propose a univariate neuroimaging index based on OT to measure such alterations. We compute the OT from each image to a template and measure the Wasserstein distance between them. By comparing the distances from all the images to the common template, we obtain a concise and informative index for each image. Our framework makes use of the Newton's method, which reduces the computational cost and enables itself to be applicable to large-scale datasets. The proposed work is a generic approach and thus may be applicable to various volumetric brain images, including structural magnetic resonance (sMR) and fluorodeoxyglucose positron emission tomography (FDG-PET) images. In the classification between Alzheimer's disease patients and healthy controls, our method achieves an accuracy of 82.30% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline sMRI dataset and outperforms several other indices. On FDG-PET dataset, we boost the accuracy to 88.37% by leveraging pairwise Wasserstein distances. In a longitudinal study, we obtain a 5% significance with p-value = 0.0000113 in a t-test on FDG-PET. The results demonstrate a great potential of the proposed index for neuroimage analysis and the precision medicine research.
S3FD: Single Shot Scale-Invariant Face Detector
Authors: Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Detection

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising
Authors: Bihan Wen, Yanjun Li, Luke Pfister, Yoram Bresler
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Denoising, Video Denoising

Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits performance in various low-level vision tasks. In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT. An efficient and unsupervised online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. We develop an efficient 3D spatio-temporal data reconstruction framework based on the proposed online learning method, which exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work of solving the proposed problem in an online fashion. We demonstrate video denoising results over commonly used videos from public datasets. Numerical experiments show that the proposed video denoising method outperforms competing methods.
Learning to Super-Resolve Blurry Face and Text Images
Authors: Xiangyu Xu, Deqing Sun, Jinshan Pan, Yu-Jin Zhang, Hanspeter Pfister, Ming-Hsuan Yang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Generative Adversarial Network, Image Restoration, Super-Resolution

We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input. This problem is highly ill-posed and the basic assumptions for existing super-resolution methods (requiring clear input) and deblurring methods (requiring high-resolution input) no longer hold. We focus on face and text images and adopt a generative adversarial network (GAN) to learn a category-specific prior to solve this problem. However, the basic GAN formulation does not generate realistic high-resolution images. In this work, we introduce novel training losses that help recover fine details. We also present a multi-class GAN that can process multi-class image restoration tasks, i.e., face and text images, using a single generator network. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art methods on both synthetic and real-world images at a lower computational cost.
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
Authors: Weixin Luo, Wen Liu, Shenghua Gao
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Anomaly Detection, Diversity

Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter selection to TSC can be avoided, meanwhile with a shallow sRNN, the reconstruction coefficients can be inferred within a forward pass, which reduces the computational cost for learning sparse coefficients. The contributions of this paper are two-fold: i) We propose a TSC, which can be mapped to a sRNN which facilitates the parameter optimization and accelerates the anomaly prediction. ii) We build a very large dataset which is even larger than the summation of all existing dataset for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset and real datasets demonstrate that our TSC based and sRNN based method consistently outperform existing methods, which validates the effectiveness of our method.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
Authors: Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Retrieval, Vehicle Re-Identification

In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
Deep Determinantal Point Process for Large-Scale Multi-Label Classification
Authors: Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, Eric P. Xing
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, Representation Learning

We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses great challenges for MLC. First, finding out the correct label subset out of exponentially many choices incurs substantial ambiguity and uncertainty. Second, the large data-size and class-size entail considerable computational cost. To address the first challenge, we investigate two strategies: capturing label-correlations from the training data and incorporating label co-occurrence relations obtained from external knowledge, which effectively eliminate semantically inconsistent labels and provide contextual clues to differentiate visually ambiguous labels. Specifically, we propose a Deep Determinantal Point Process (DDPP) model which seamlessly integrates a DPP with deep neural networks (DNNs) and supports end-to-end multi-label learning and deep representation learning. The DPP is able to capture label-correlations of any order with a polynomial computational cost, while the DNNs learn hierarchical features of images/videos and capture the dependency between input data and labels. To incorporate external knowledge about label co-occurrence relations, we impose a relational regularization over the kernel matrix in DDPP. To address the second challenge, we study an efficient low-rank kernel learning algorithm based on inducing point methods. Experiments on the two datasets demonstrate the efficacy and efficiency of the proposed methods.
DualNet: Learn Complementary Features for Image Recognition
Authors: Saihui Hou, Xu Liu, Zilei Wang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

In this work we propose a novel framework named DualNet aiming at learning more accurate representation for image recognition. Here two parallel neural networks are coordinated to learn complementary features and thus a wider network is constructed. Specifically, we logically divide an end-to-end deep convolutional neural network into two functional parts, i.e., feature extractor and image classifier. The extractors of two subnetworks are placed side by side, which exactly form the feature extractor of DualNet. Then the two-stream features are aggregated to the final classifier for overall classification, while two auxiliary classifiers are appended behind the feature extractor of each subnetwork to make the separately learned features discriminative alone. The complementary constraint is imposed by weighting the three classifiers, which is indeed the key of DualNet. The corresponding training strategy is also proposed, consisting of iterative training and joint finetuning, to make the two subnetworks cooperate well with each other. Finally, DualNet based on the well-known CaffeNet, VGGNet, NIN and ResNet are thoroughly investigated and experimentally evaluated on multiple datasets including CIFAR-100, Stanford Dogs and UEC FOOD-100. The results demonstrate that DualNet can really help learn more accurate image representation, and thus result in higher accuracy for recognition. In particular, the performance on CIFAR-100 is state-of-the-art compared to the recent works.
Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization
Authors: Sijia Cai, WangMeng Zuo, Lei Zhang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Fine-Grained Visual Categorization

The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these issues, we propose an end-to-end framework based on higher-order integration of hierarchical convolutional activations for FGVC. By treating the convolutional activations as local descriptors, hierarchical convolutional activations can serve as a representation of local parts from different scales. A polynomial kernel based predictor is proposed to capture higher-order statistics of convolutional activations for modeling part interaction. To model inter-layer part interactions, we extend polynomial predictor to integrate hierarchical activations via kernel fusion. Our work also provides a new perspective for combining convolutional activations from multiple layers. While hypercolumns simply concatenate maps from different layers, and holistically-nested network uses weighted fusion to combine side-outputs, our approach exploits higher-order intra-layer and inter-layer relations for better integration of hierarchical convolutional features. The proposed framework yields more discriminative representation and achieves competitive results on the widely used FGVC datasets.
VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
Authors: Saihui Hou, Yushan Feng, Zilei Wang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Fine-Grained Visual Categorization, Management

VegFru: A Domain-Specific Dataset for Fine-grained Visual Categorization In this paper, we propose a novel domain-specific dataset named VegFru for fine-grained visual categorization (FGVC). While the existing datasets for FGVC are mainly focused on animal breeds or man-made objects with limited labelled data, VegFru is a larger dataset consisting of vegetables and fruits which are closely associated with the daily life of everyone. Aiming at domestic cooking and food management, VegFru categorizes vegetables and fruits according to their eating characteristics, and each image contains at least one edible part of vegetables or fruits with the same cooking usage. Particularly, all the images are labelled hierarchically. The current version covers vegetables and fruits of 25 upper-level categories and 292 subordinate classes. And it contains more than 160,000 images in total and at least 200 images for each subordinate class. Accompanying the dataset, we also propose an effective framework called HybridNet to exploit the label hierarchy for FGVC. Specifically, multiple granularity features are first extracted by dealing with the hierarchical labels separately. And then they are fused through explicit operation, e.g., Compact Bilinear Pooling, to form a unified representation for the ultimate recognition. The experimental results on the novel VegFru, the public FGVC-Aircraft and CUB-200-2011 indicate that HybridNet achieves one of the top performance on these datasets. The dataset and code are available at https://github.com/hshustc/vegfru.
Increasing CNN Robustness to Occlusions by Reducing Filter Support
Authors: Elad Osherov, Michael Lindenbaum
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, Object

Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and occlusions may be computationally expensive. This work proposes an alternative approach to increasing the robustness of CNNs to occlusion. We start by studying the effect of partial occlusions on the trained CNN and show, empirically, that training on partially occluded examples reduces the spatial support of the filters. Building upon this finding, we argue that smaller filter support is beneficial for occlusion robustness. We propose a training process that uses a special regularization term that acts to shrink the spatial support of the filters. We consider three possible regularization terms that are based on second central moments, group sparsity, and mutually reweighted L1, respectively. When trained on normal (unoccluded) examples, the resulting classifier is highly robust to occlusions. For large training sets and limited training time, the proposed classifier is even more accurate than standard classifiers trained on occluded object examples.