pwc-archive/papers-with-abstracts huggingface dataset:
Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer
Authors: Yu-Xing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Semi-Supervised Object Detection, Transfer Learning

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting.
Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers
Authors: Fan Yang, Wongun Choi, Yuanqing Lin
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: All, Object, object-detection, Object Detection

In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional features depending on the scale of candidate object proposals. The cascaded rejection classifiers (CRC) effectively utilize convolutional features and eliminate negative object proposals in a cascaded manner, which greatly speeds up the detection while maintaining high accuracy. In combination of the two, our method achieves significantly better accuracy compared to other state-of-the-arts in three challenging datasets, PASCAL object detection challenge, KITTI object detection benchmark and newly collected Inner-city dataset, while being more efficient.
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection
Authors: Keze Wang, Liang Lin, WangMeng Zuo, Shuhang Gu, Lei Zhang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Novel Object Detection, Object, object-detection, Object Detection, Representation Learning

Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN [12]), and achieves new state-of-the-arts.
Monocular 3D Object Detection for Autonomous Driving
Authors: Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, Raquel Urtasun
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Object Detection, Autonomous Driving, Monocular 3D Object Detection, Object, object-detection, Object Detection, Object Proposal Generation, Semantic Segmentation, Vehicle Pose Estimation

The goal of this paper is to perform 3D object detection in single monocular images in the domain of autonomous driving.  Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain high-quality object detections. The focus of this paper is on proposal generation. In particular, we propose a probabilistic model that places object candidates in 3D using a prior on ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials such as semantic segmentation, contextual information, size and location priors and typical object shape. The weights in our model are trained with S-SVM. Experiments show that our object proposal generation approach significantly outperforms all monocular baselines, and achieves the best detection performance on the challenging KITTI benchmark, among the published monocular competitors.
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles
Authors: Hongye Liu, Yonghong Tian, Yaowei Yang, Lu Pang, Tiejun Huang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Recognition, Person Re-Identification, Retrieval, Unsupervised Domain Adaptation, Vehicle Re-Identification

The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized large-scale image database "VehicleID", which includes multiple images of the same vehicle captured by different real-world cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.
Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations
Authors: Viktoriia Sharmanska, Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Novi Quadrianto
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification

Imagine we show an image to a person and ask her/him to decide whether the scene in the image is warm or not warm, and whether it is easy or not to spot a squirrel in the image. For exactly the same image, the answers to those questions are likely to differ from person to person. This is because the task is inherently ambiguous. Such an ambiguous, therefore challenging, task is pushing the boundary of computer vision in showing what can and can not be learned from visual data. Crowdsourcing has been invaluable for collecting annotations. This is particularly so for a task that goes beyond a clear-cut dichotomy as multiple human judgments per image are needed to reach a consensus. This paper makes conceptual and technical contributions. On the conceptual side, we define disagreements among annotators as privileged information about the data instance. On the technical side, we propose a framework to incorporate annotation disagreements into the classifiers. The proposed framework is simple, relatively fast, and outperforms classifiers that do not take into account the disagreements, especially if tested on high confidence annotations.
A Task-Oriented Approach for Cost-Sensitive Recognition
Authors: Roozbeh Mottaghi, Hannaneh Hajishirzi, Ali Farhadi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Scene Understanding

With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications. These applications, unlike general scene understanding, are task oriented and require specific information from visual data. Considering the current growth in new sensory devices, feature designs, feature learning methods, and algorithms, the search in the space of features and models becomes combinatorial. In this paper, we propose a novel cost-sensitive task-oriented recognition method that is based on a combination of linguistic semantics and visual cues. Our task-oriented framework is able to generalize to unseen tasks for which there is no training data and outperforms state-of-the-art cost-based recognition baselines on our new task-based dataset.
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning
Authors: Ali Borji, Saeed Izadi, Laurent Itti
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Domain Adaptation, Object Recognition, Transfer Learning

Tolerance to image variations (e.g. translation, scale, pose, illumination, background) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision especially with the emergence of highly popular deep learning models. While being very useful for learning invariance to object inter- and intra-class shape variability, these large-scale wild datasets are not very useful for learning  invariance to other parameters urging researchers to resort to other tricks for training a model. In this work, we introduce a large-scale synthetic dataset, which is freely and publicly available, and use it to answer several fundamental questions regarding selectivity and invariance properties of convolutional neural networks. Our dataset contains two parts: a) objects shot on a turntable: 15 categories, 8 rotation angles, 11 cameras on a semi-circular arch, 5 lighting conditions, 3 focus levels, variety of backgrounds (23.4 per instance) generating 1320 images per instance (about 22 million images in total), and b) scenes: in which a robotic arm takes pictures of objects on a 1:160 scale scene. We study: 1) invariance and selectivity of different CNN layers, 2) knowledge transfer from one object category to another, 3) systematic or random sampling of images to build a train set, 4) domain adaptation from synthetic to natural scenes, and 5) order of knowledge delivery to CNNs. We also discuss how our analyses can lead the field to develop more efficient deep learning methods.
Deep Decision Network for Multi-Class Image Classification
Authors: Venkatesh N. Murthy, Vivek Singh, Terrence Chen, R. Manmatha, Dorin Comaniciu
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we present a novel Deep Decision Network (DDN) that provides an alternative approach towards building an efficient deep learning network. During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks. This results in a tree-like structured network driven by the data. The proposed method provides an insight into the data by identifying the group of classes that are hard to classify and require more attention when compared to others. DDN also has the ability to make early decisions thus making it suitable for time-sensitive applications.  We validate DDN on two publicly available benchmark datasets: CIFAR-10 and CIFAR-100 and it yields state-of-the-art classification performance on both the datasets. The proposed algorithm has no limitations to be applied to any generic classification problems.
Fast Algorithms for Linear and Kernel SVM+
Authors: Wen Li, Dengxin Dai, Mingkui Tan, Dong Xu, Luc van Gool
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Form

The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the   training data. In this paper, we propose two efficient algorithms for solving the linear and kernel SVM+, respectively. For linear SVM+, we absorb the bias term into the weight vector, and formulate a new optimization problem with simpler constraints in the dual form. Then, we develop an efficient dual coordinate descent algorithm to solve the new optimization problem. For kernel SVM+, we further apply the l2-loss, which leads to a simpler optimization problem in the dual form with only half of dual variables when compared with the dual form of the original SVM+ method. More interestingly, we show that our new dual problem can be efficiently solved by using the SMO algorithm of the one-class SVM problem. Comprehensive experiments on three datasets clearly demonstrate that our proposed algorithms achieve significant speed-up than the state-of-the-art solvers for linear and kernel SVM+.
Hierarchically Gated Deep Networks for Semantic Segmentation
Authors: Guo-Jun Qi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Semantic Segmentation

Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions.   While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels.  Multi-scale Convolutional Neural Networks (CNNs) and their variants have made striking success for modeling the global scene structure for an image. However, they are limited in labeling fine-grained local structures like pixels and patches, since spatial contexts might be  blindly mixed up without appropriately customizing their scales.  To address this challenge, we develop a novel paradigm of multi-scale deep network to model spatial contexts surrounding different pixels at various scales.  It builds multiple layers of memory cells, learning feature representations for individual pixels at their customized scales by hierarchically absorbing relevant spatial contexts via memory gates between layers.Such Hierarchically Gated Deep Networks (HGDNs) can customize a suitable scale for each pixel, thereby delivering better performance on labeling scene structures of various scales.  We conduct the experiments on two datasets, and show competitive results compared with the other multi-scale deep networks on the semantic segmentation task.
CNN-N-Gram for Handwriting Word Recognition
Authors: Arik Poznanski, Lior Wolf
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: All, Handwriting Recognition

Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams and trigrams are estimated for the entire word and for parts of it. Canonical Correlation Analysis is then used to match the estimated profile to the true profiles of all words in a large dictionary. The CNN that is used employs several novelties such as the use of multiple fully connected branches. Applied to all commonly used handwriting recognition benchmarks, our method outperforms, by a very large margin, all existing methods.
Real-Time Salient Object Detection With a Minimum Spanning Tree
Authors: Wei-Chih Tu, Shengfeng He, Qingxiong Yang, Shao-Yi Chien
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Video Salient Object Detection

In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy.
Local Background Enclosure for RGB-D Salient Object Detection
Authors: David Feng, Nick Barnes, ShaoDi You, Chris McCarthy
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly variable in depth. Here, we propose a novel RGB-D saliency feature. Local Background Enclosure (LBE) captures the spread of angular directions which are background with respect to the candidate region and the object that it is part of. We show that our feature improves over state-of-the-art RGB-D saliency approaches as well as RGB methods on the RGBD1000 and NJUDS2000 datasets.
Semantic Channels for Fast Pedestrian Detection
Authors: Arthur Daniel Costea, Sergiu Nedevschi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: CPU, GPU, Pedestrian Detection, Segmentation, Semantic Segmentation

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation.  As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust  segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.
Recurrent Face Aging
Authors: Wei Wang, Zhen Cui, Yan Yan, Jiashi Feng, Shuicheng Yan, Xiangbo Shu, Nicu Sebe
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Verification

Modeling the aging process of human face is important for cross-age face verification and recognition. In this paper, we introduce a recurrent face aging (RFA) framework based on a recurrent neural network which can identify the ages of people from 0 to 80. Due to the lack of labeled face data of the same person captured in a long range of ages, traditional face aging models usually split the ages into discrete groups and learn a one-step face feature transformation for each pair of adjacent age groups. However, those methods neglect the in-between evolving states between the adjacent age groups and the synthesized faces often suffer from severe ghosting artifacts. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transition states. In this way, the ghosting artifacts can be effectively eliminated and the intermediate aged faces between two discrete age groups can also be obtained. Towards this target, we employ a two-layer gated recurrent unit as the basic recurrent module whose bottom layer encodes a young face to a latent representation and the top layer decodes the representation to a corresponding older face. The experimental results demonstrate our proposed RFA provides better aging faces over other state-of-the-art age progression methods.
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions
Authors: Sergey Tulyakov, Xavier Alameda-Pineda, Elisa Ricci, Lijun Yin, Jeffrey F. Cohn, Nicu Sebe
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Heart rate estimation, Matrix Completion

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state-of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of-the-art HR estimation methods in naturalistic conditions.