pwc-archive/papers-with-abstracts huggingface dataset:
Global Refinement of Random Forest
Authors: Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce  the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.
Adaptive Region Pooling for Object Detection
Authors: Yi-Hsuan Tsai, Onur C. Hamsici, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection

Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity. We address this variability by a novel feature pooling method that is adaptive to segmented regions. The proposed detection algorithm automatically discovers a diverse set of exemplars and their distinctive parts which are used to encode the region structure by the proposed feature pooling method. Based on each exemplar and its parts, a regression model is learned with samples selected by a coarse region matching scheme. The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms. We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features. We also show that object information can be transferred through exemplars for detected objects.
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning
Authors: Mohammad Rastegari, Hannaneh Hajishirzi, Ali Farhadi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Instance Learning, Text Categorization

In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are {\it highly and consistently similar} to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL.  Our experiments demonstrate that our method  consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Authors: Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, DaCheng Tao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Tracking

Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter (ICF) is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control. MUSTer was extensively evaluated on the CVPR2013 Online Object Tracking Benchmark (OOTB) and ALOV++ datasets. The experimental results demonstrated the superior performance of MUSTer in comparison with other state-of-art trackers.
Complexity-Adaptive Distance Metric for Object Proposals Generation
Authors: Yao Xiao, Cewu Lu, Efstratios Tsougenis, Yongyi Lu, Chi-Keung Tang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, Superpixels

Distance metric plays a key role in grouping superpixels to produce object proposals for object detection. We observe that existing distance metrics work primarily for low complexity cases. In this paper, we develop a novel distance metric for grouping two superpixels in high-complexity scenarios. Combining them, a complexity-adaptive distance measure is produced that achieves improved grouping in different levels of complexity. Our extensive experimentation shows that our method can achieve good results in the PASCAL VOC 2012 dataset surpassing the latest state-of-the-art methods.
High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild
Authors: Xiangyu Zhu, Zhen Lei, Junjie Yan, Dong Yi, Stan Z. Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition, Vocal Bursts Intensity Prediction

Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance. An ideal normalization method is desired to be automatic, database independent and high-fidelity, where the face appearance should be preserved with little artifact and information loss. However, most normalization methods fail to satisfy one or more of the goals. In this paper, we propose a High-fidelity Pose and Expression Normalization (HPEN) method with 3D Morphable Model (3DMM) which can automatically generate a natural face image in frontal pose and neutral expression. Specifically, we firstly make a landmark marching assumption to describe the non-correspondence between 2D and 3D landmarks caused by pose variations and propose a pose adaptive 3DMM fitting algorithm. Secondly, we mesh the whole image into a 3D object and eliminate the pose and expression variations using an identity preserving 3D transformation. Finally, we propose an inpainting method based on Possion Editing to fill the invisible region caused by self occlusion. Extensive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recognition performance and outperforms state-of-the-art methods in both constrained and unconstrained environments.
Transformation of Markov Random Fields for Marginal Distribution Estimation
Authors: Masaki Saito, Takayuki Okatani
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper presents a generic method for transforming MRFs for the marginal inference problem. Its major application is to downsize MRFs to speed up the computation. Unlike the MAP inference, there are only classical algorithms for the marginal inference problem such as BP etc. that require large computational cost. Although downsizing MRFs should directly reduce the computational cost, there is no systematic way of doing this, since it is unclear how to obtain the MRF energy for the downsized MRFs and also how to translate the estimates of their marginal distributions to those of the original MRFs. The proposed method resolves these issues by a novel probabilistic formulation of MRF transformation. The key idea is to represent the joint distribution of an MRF with that of the transformed one, in which the variables of the latter are treated as latent variables. We also show that the proposed method can be applied to discretization of variable space of continuous MRFs and can be used with Markov chain Monte Carlo methods. The experimental results demonstrate the effectiveness of the proposed method.
Sparse Convolutional Neural Networks
Authors: Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: CPU, image-classification, Image Classification, object-detection, Object Detection

Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and intra-channel redundancy, with a fine-tuning step that minimize the recognition loss caused by maximizing sparsity. This procedure zeros out more than 90\% of parameters, with a drop of accuracy that is less than 1\% on the ILSVRC2012 dataset.  We also propose an efficient sparse matrix multiplication algorithm on CPU for Sparse Convolutional Neural Networks (SCNN) models. Our CPU implementation demonstrates much higher efficiency than the off-the-shelf sparse matrix libraries, with a significant speedup realized over the original dense network. In addition, we apply the SCNN model to the object detection problem, in conjunction with a cascade model and sparse fully connected layers, to achieve significant speedups.
Cascaded Hand Pose Regression
Authors: Xiao Sun, Yichen Wei, Shuang Liang, Xiaoou Tang, Jian Sun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Object, Pose Estimation, regression

We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features that generalize the previous 2D parameterized features and achieve better invariance to 3D transformations. Our second contribution is a principled hierarchical regression that is adapted to the articulated object structure. It is therefore more accurate and faster. Comprehensive experiments verify the state-of-the-art accuracy and efficiency of the proposed approach on the challenging 3D hand pose estimation problem, on a public dataset and our new dataset.
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
Authors: Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Crowd Counting

Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression
Authors: Li Wan, David Eigen, Rob Fergus
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection

Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection.  Yet these two approaches find their strengths in  complementary areas: DPMs are well-versed in object composition, modeling fine-grained spatial relationships between parts; likewise, ConvNets are adept at producing powerful image features, having been discriminatively trained directly on the pixels. In this paper, we propose a new model that combines these two approaches, obtaining the advantages of each. We train this model using a new structured loss function that considers all bounding boxes within an image, rather than isolated object instances. This enables the non-maximal suppression (NMS) operation, previously treated as a separate post-processing stage, to be integrated into the model. This allows for discriminative training of our combined Convnet + DPM + NMS model in end-to-end fashion. We evaluate our system on PASCAL VOC 2007 and 2011 datasets, achieving competitive results on both bench- marks.
A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
Authors: Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, Andrew C. Gallagher
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper explores two new aspects of photos and human emotions. First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image. Our studies also show that the same person may have multiple emotional reactions to one image. Predicting emotions in ``distributions'' instead of a single dominant emotion is important for many applications. Second, we show not only that we can often change the evoked emotion of an image by adjusting color tone and texture related features but also that we can choose in which ``emotional direction" this change occurs by selecting a target image. In addition, we present a new database, Emotion6, containing distributions of emotions.
Part-Based Modelling of Compound Scenes From Images
Authors: Anton van den Hengel, Chris Russell, Anthony Dick, John Bastian, Daniel Pooley, Lachlan Fleming, Lourdes Agapito
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We propose a method to recover the structure of a compound scene from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined library, each with an associated pose. This has several advantages over a volume or mesh representation both for estimation and the utility of the recovered model. The main challenge in recovering such a model is the combinatorial number of possible arrangements of parts. We address this issue by exploiting the intrinsic structure and sparsity of the problem, and show that our method scales to scenes constructed from large libraries of parts.
Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion
Authors: Olga Veksler
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Potts energy  frequently occurs in  computer vision applications.  We present an efficient parallel method for optimizing Potts energy based on the extension of hierarchical fusion algorithm.  Unlike previous parallel  graph-cut based optimization algorithms, our approach has optimality bounds even after a single iteration over all labels, i.e. after solving only k-1 max-flow problems, where k is the number of labels. This is perhaps the minimum number of max-flow problems one has to solve to obtain a solution with optimality guarantees.  Our approximation factor is O(log k). Although this is not as good as the factor of 2 approximation of the well known expansion algorithm,  we achieve very good results in practice. In particular, we found that the results of our algorithm after one iteration are always better than the results after one iteration of the expansion algorithm. We demonstrate  experimentally the computational advantages of our parallel implementation on the problem of stereo correspondence, achieving a factor of 1.5 to 2.6 speedup compared to the serial implementation.  These results were obtained with a small number of processors.  The expected speedups with a larger number of processors are greater.
Elastic-Net Regularization of Singular Values for Robust Subspace Learning
Authors: Eunwoo Kim, Minsik Lee, Songhwai Oh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Learning a low-dimensional structure plays an important role in computer vision. Recently, a new family of methods, such as l1 minimization and robust principal component analysis, has been proposed for low-rank matrix approximation problems and shown to be robust against outliers and missing data. But these methods often require heavy computational load and can fail to find a solution when highly corrupted data are presented. In this paper, an elastic-net regularization based low-rank matrix factorization method for subspace learning is proposed. The proposed method finds a robust solution efficiently by enforcing a strong convex constraint to improve the algorithm's stability while maintaining the low-rank property of the solution. It is shown that any stationary point of the proposed algorithm satisfies the Karush-Kuhn-Tucker optimality conditions. The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
Hardware Compliant Approximate Image Codes
Authors: Da Kuang, Alex Gittens, Raffay Hamid
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency, General Classification

In recent years, several feature encoding schemes for the bags-of-visual-words model have been proposed. While most of these schemes produce impressive results, they all share an important limitation: their high computational complexity makes it challenging to use them for large-scale problems. In this work, we propose an approximate locality-constrained encoding scheme that offers significantly better computational efficiency (~40x) than its exact counterpart, with comparable classification accuracy. Using the perturbation analysis of least-squares problems, we present a formal approximation error analysis of our approach, which helps distill the intuition behind the robustness of our method. We present a thorough set of empirical analyses on multiple standard data-sets, to assess the capability of our encoding scheme for its representational as well as discriminative accuracy.
Photometric Refinement of Depth Maps for Multi-Albedo Objects
Authors: Avishek Chatterjee, Venu Madhav Govindu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

In this paper, we propose a novel uncalibrated photometric method for refining depth maps of multi-albedo objects obtained from consumer depth cameras like Kinect. Existing uncalibrated photometric methods either assume that the object has constant albedo or rely on segmenting images into constant albedo regions. The method of this paper does not require the constant albedo assumption and we believe it is the first work of its kind to handle objects with arbitrary albedo under uncalibrated illumination. We first robustly estimate a rank 3 approximation of the observed brightness matrix using an iterative reweighting method. Subsequently, we factorize this rank reduced brightness matrix into the corresponding lighting, albedo and surface normal components. The proposed factorization is shown to be convergent. We experimentally demonstrate the value of our approach by presenting highly accurate three-dimensional reconstructions of a wide variety of objects. Additionally, since any photometric method requires a radiometric calibration of the camera used, we also present a direct radiometric calibration technique for the infra-red camera of the structured-light stereo depth scanner. Unlike existing methods, this calibration technique does not depend on a known calibration object or on the properties of the scene illumination used.
Classifier Based Graph Construction for Video Segmentation
Authors: Anna Khoreva, Fabio Galasso, Matthias Hein, Bernt Schiele
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Diversity, graph construction, graph partitioning, Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling topperformance on recent benchmarks, consist of three essential components: 1. powerful features account for object appearance and motion similarities; 2. spatio-temporal neighborhoods of pixels or superpixels (the graph edges) are modeled using a combination of those features; 3. video segmentation is formulated as a graph partitioning problem. While a wide variety of features have been explored and various graph partition algorithms have been proposed, there is surprisingly little research on how to construct a graph to obtain the best video segmentation performance. This is the focus of our paper. We propose to combine features by means of a classifier, use calibrated classifier outputs as edge weights and define the graph topology by edge selection. By learning the graph (without changes to the graph partitioning method), we improve the results of the best performing video segmentation algorithm by 6% on the challenging VSB100 benchmark, while reducing its runtime by 55%, as the learnt graph is much sparser.
ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding
Authors: Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, Juan Carlos Niebles
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Detection, Action Recognition, Activity Detection, General Classification, Temporal Action Localization, Video Classification

In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper, we introduce ActivityNet: a new large-scale video benchmark for human activity understanding. Our new benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. In its current version, ActivityNet provides samples from 203 activity categories with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 hours of video. We illustrate three scenarios in which ActivityNet can be used to benchmark and compare algorithms for human activity understanding: untrimmed video classification, trimmed activity classification and activity detection.