pwc-archive/papers-with-abstracts huggingface dataset:
Deep Embedding Learning With Discriminative Sampling Policy
Authors: Yueqi Duan,  Lei Chen,  Jiwen Lu,  Jie Zhou
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

Deep embedding learning aims to learn a distance metric for effective similarity measurement, which has achieved promising performance in various tasks. As the vast majority of training samples produce gradients with magnitudes close to zero, hard example mining is usually employed to improve the effectiveness and efficiency of the training procedure. However, most existing sampling methods are designed by hand, which ignores the dependence between examples and suffer from exhaustive searching. In this paper, we propose a deep embedding with discriminative sampling policy (DE-DSP) learning framework by simultaneously training two models: a deep sampler network that learns effective sampling strategies, and a feature embedding that maps samples to the feature space. Rather than exhaustively calculating the hardness of all the examples for mining through forward-propagation, the deep sampler network exploits the strong prior of relations among samples to learn discriminative sampling policy in an more efficient manner. Experimental results demonstrate faster convergence and stronger discriminative power of our DE-DSP framework under different embedding objectives.
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations
Authors: Wonhee Lee,  Joonil Na,  Gunhee Kim
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Task Learning, Novel Object Detection, Object, object-detection, Object Detection, Region Proposal, Self-Supervised Learning

In spite of recent enormous success of deep convolutional networks in object detection, they require a large amount of bounding box annotations, which are often time-consuming and error-prone to obtain. To make better use of given limited labels, we propose a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL). We propose a set of auxiliary tasks that help improve the accuracy of object detection. They create their own labels by recycling the bounding box labels (i.e. annotations of the main task) in an SSL manner, and are jointly trained with the object detection model in an MTL way. Our approach is integrable with any region proposal based detection models. We empirically validate that our approach effectively improves detection performance on various architectures and datasets. We test two state-of-the-art region proposal object detectors, including Faster R-CNN and R-FCN, with three CNN backbones of ResNet-101, Inception-ResNet-v2, and MobileNet on two benchmark datasets of PASCAL VOC and COCO.
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis
Authors: Chao Chen,  Guanbin Li,  Ruijia Xu,  Tianshui Chen,  Meng Wang,  Liang Lin
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Classification, 3D Object Recognition, Clustering, Object Recognition

Current neural networks for 3D object recognition are vulnerable to 3D rotation. Existing works mostly rely on massive amounts of rotation-augmented data to alleviate the problem, which lacks solid guarantee of the 3D rotation invariance. In this paper, we address the issue by introducing a novel point cloud representation that can be mathematically proved rigorously rotation-invariant, i.e., identical point clouds in different orientations are unified as a unique and consistent representation. Moreover, the proposed representation is conditional information-lossless, because it retains all necessary information of point cloud except for orientation information. In addition, the proposed representation is complementary with existing network architectures for point cloud and fundamentally improves their robustness against rotation transformation. Finally, we propose a deep hierarchical cluster network called ClusterNet to better adapt to the proposed representation. We employ hierarchical clustering to explore and exploit the geometric structure of point cloud, which is embedded in a hierarchical structure tree. Extensive experimental results have shown that our proposed method greatly outperforms the state-of-the-arts in rotation robustness on rotation-augmented 3D object classification benchmarks.
Deep Global Generalized Gaussian Networks
Authors: Qilong Wang,  Peihua Li,  Qinghua Hu,  Pengfei Zhu,  Wangmeng Zuo
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: parameter estimation, Rolling Shutter Correction

Recently, global covariance pooling (GCP) has shown great advance in improving classification performance of deep convolutional neural networks (CNNs). However, existing deep GCP networks compute covariance pooling of convolutional activations with assumption that activations are sampled from Gaussian distributions, which may not hold in practice and fails to fully characterize the statistics of activations. To handle this issue, this paper proposes a novel deep global generalized Gaussian network (3G-Net), whose core is to estimate a global covariance of generalized Gaussian for modeling the last convolutional activations. Compared with GCP in Gaussian setting, our 3G-Net assumes the distribution of activations follows a generalized Gaussian, which can capture more precise characteristics of activations. However, there exists no analytic solution for parameter estimation of generalized Gaussian, making our 3G-Net challenging. To this end, we first present a novel regularized maximum likelihood estimator for robust estimating covariance of generalized Gaussian, which can be optimized by a modified iterative re-weighted method. Then, to efficiently estimate the covariance of generaized Gaussian under deep CNN architectures, we approximate this re-weighted method by developing an unrolling re-weighted module and a square root covariance layer. In this way, 3GNet can be flexibly trained in an end-to-end manner. The experiments are conducted on large-scale ImageNet-1K and Places365 datasets, and the results demonstrate our 3G-Net outperforms its counterparts while achieving very competitive performance to state-of-the-arts.
Dynamic Recursive Neural Network
Authors: Qiushan Guo,  Zhipeng Yu,  Yichao Wu,  Ding Liang,  Haoyu Qin,  Junjie Yan
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network. Different from forwarding through different blocks sequentially in previous networks, we demonstrate that the DRNN can achieve better performance with fewer blocks by employing block recursively. We further add a gate structure to each block, which can adaptively decide the loop times of recursive blocks to reduce the computational cost.  Since the recursive networks are hard to train, we propose the Loopy Variable Batch Normalization (LVBN) to stabilize the volatile gradient. Further, we improve the LVBN to correct statistical bias caused by the gate structure.  Experiments show that the DRNN reduces the parameters and computational cost and while outperforms the original model in term of the accuracy consistently on CIFAR-10 and ImageNet-1k. Lastly we visualize and discuss the relation between image saliency and the number of loop time.
Destruction and Construction Learning for Fine-Grained Image Recognition
Authors: Yue Chen,  Yalong Bai,  Wei Zhang,  Tao Mei
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Fine-Grained Image Classification, Fine-Grained Image Recognition, General Classification

Delicate feature representation about object parts plays a critical role in fine-grained recognition. For example, experts can even distinguish fine-grained objects relying only on object parts according to professional knowledge. In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge. Besides the standard classification backbone network, another "destruction and construction" stream is introduced to carefully "destruct" and then "reconstruct" the input image, for learning discriminative regions and features. More specifically, for "destruction", we first partition the input image into local regions and then shuffle them by a Region Confusion Mechanism (RCM). To correctly recognize these destructed images, the classification network has to pay more attention to discriminative regions for spotting the differences. To compensate the noises introduced by RCM, an adversarial loss, which distinguishes original images from destructed ones, is applied to reject noisy patterns introduced by RCM. For "construction", a region alignment network, which tries to restore the original spatial layout of local regions, is followed to model the semantic correlation among local regions. By jointly training with parameter sharing, our proposed DCL injects more discriminative local details to the classification network. Experimental results show that our proposed framework achieves state-of-the-art performance on three standard benchmarks. Moreover, our proposed method does not need any external knowledge during training, and there is no computation overhead at inference time except the standard classification network feed-forwarding. Source code: https://github.com/JDAI-CV/DCL.
Distraction-Aware Shadow Detection
Authors: Quanlong Zheng,  Xiaotian Qiao,  Ying Cao,  Rynson W.H. Lau
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Understanding, Shadow Detection

Shadow detection is an important and challenging task for scene understanding. Despite promising results from recent deep learning based methods. Existing works still struggle with ambiguous cases where the visual appearances of shadow and non-shadow regions are similar (referred to as distraction in our context). In this paper, we propose a Distraction-aware Shadow Detection Network (DSDNet) by explicitly learning and integrating the semantics of visual distraction regions in an end-to-end framework. At the core of our framework is a novel standalone, differentiable Distraction-aware Shadow (DS) module, which allows us to learn distraction-aware, discriminative features for robust shadow detection, by explicitly predicting false positives and false negatives. We conduct extensive experiments on three public shadow detection datasets, SBU, UCF and ISTD, to evaluate our method. Experimental results demonstrate that our model can boost shadow detection performance, by effectively suppressing the detection of false positives and false negatives, achieving state-of-the-art results.
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection
Authors: Wei Liu,  Shengcai Liao,  Weiqiang Ren,  Weidong Hu,  Yinan Yu
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, Pedestrian Detection, Vocal Bursts Intensity Prediction

Object detection generally requires sliding-window classifiers in tradition or anchor-based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in windows or anchors. In this paper, taking pedestrian detection as an example, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are pedestrians, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the pedestrian points, which is also a straightforward convolution. Therefore, in this paper, pedestrian detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys an anchor-free setting. Though structurally simple, it presents competitive accuracy and good speed on challenging pedestrian detection benchmarks, and hence leading to a new attractive pedestrian detector. Code and models will be available at https://github.com/liuwei16/CSP.
RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection
Authors: Leonid Karlinsky,  Joseph Shtok,  Sivan Harary,  Eli Schwartz,  Amit Aides,  Rogerio Feris,  Raja Giryes,  Alex M. Bronstein
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Few-Shot Object Detection, General Classification, Metric Learning, Object, object-detection, Object Detection

Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.
KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing
Authors: Mengshi Qi,  Yunhong Wang,  Jie Qin,  Annan Li
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Parsing

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.
Interactive Image Segmentation via Backpropagating Refinement Scheme
Authors: Won-Dong Jang,  Chang-Su Kim
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.
Scene Parsing via Integrated Classification Model and Variance-Based Regularization
Authors: Hengcan Shi,  Hongliang Li,  Qingbo Wu,  Zichen Song
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, General Classification, Scene Parsing, Scene Segmentation, Semantic Segmentation

Scene Parsing is a challenging task in computer vision, which can be formulated as a pixel-wise classification problem. Existing deep-learning-based methods usually use one general classifier to recognize all object categories. However, the general classifier easily makes some mistakes in dealing with some confusing categories that share similar appearances or semantics. In this paper, we propose an integrated classification model and a variance-based regularization to achieve more accurate classifications. On the one hand, the integrated classification model contains multiple classifiers, not only the general classifier but also a refinement classifier to distinguish the confusing categories. On the other hand, the variance-based regularization differentiates the scores of all categories as large as possible to reduce misclassifications. Specifically, the integrated classification model includes three steps. The first is to extract the features of each pixel. Based on the features, the second step is to classify each pixel across all categories to generate a preliminary classification result. In the third step, we leverage a refinement classifier to refine the classification result, focusing on differentiating the high-preliminary-score categories. An integrated loss with the variance-based regularization is used to train the model. Extensive experiments on three common scene parsing datasets demonstrate the effectiveness of the proposed method.
Surface Reconstruction From Normals: A Robust DGP-Based Discontinuity Preservation Approach
Authors: Wuyuan Xie,  Miaohui Wang,  Mingqiang Wei,  Jianmin Jiang,  Jing Qin
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Surface Reconstruction

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.
Creative Flow+ Dataset
Authors: Maria Shugrina,  Ziheng Liang,  Amlan Kar,  Jiaman Li,  Angad Singh,  Karan Singh,  Sanja Fidler
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Character Animation From A Single Photo, Depth Estimation, Image Animation, Object Tracking, Optical Flow Estimation, Style Generalization, Surface Normals Estimation, Video Style Transfer, Video Understanding

We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth. Our dataset includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. Our dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size. While modern techniques for tasks such as optical flow estimation achieve impressive performance on realistic images and video, today there is no way to gauge their performance on non-photorealistic images. Creative Flow+ poses a new challenge to generalize real-world Computer Vision to messy stylized content. We show that learning-based optical flow methods fail to generalize to this data and struggle to compete with classical approaches, and invite new research in this area.  Our dataset and a new optical flow benchmark will be publicly available at: www.cs.toronto.edu/creativeflow/. We further release the complete dataset creation pipeline, allowing the community to generate and stylize their own data on demand.
Efficient Multi-Domain Learning by Covariance Normalization
Authors: Yunsheng Li,  Nuno Vasconcelos
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

The problem of multi-domain learning of deep networks is considered. An adaptive layer is induced per target domain and a novel procedure, denoted covariance normalization (CovNorm), proposed to reduce its parameters. CovNorm is a data driven method of fairly simple implementation, requiring two principal component analyzes (PCA) and fine-tuning of a mini-adaptation layer. Nevertheless, it is shown, both theoretically and experimentally, to have several advantages over previous approaches, such as batch normalization or geometric matrix approximations. Furthermore, CovNorm can be deployed both when target datasets are available sequentially or simultaneously. Experiments show that, in both cases, it has performance comparable to a fully fine-tuned network, using as few as 0.13% of the corresponding parameters per target domain.
Predicting Visible Image Differences Under Varying Display Brightness and Viewing Distance
Authors: Nanyang Ye,  Krzysztof Wolski,  Rafal K. Mantiuk
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

Numerous applications require a robust metric that can predict whether image differences are visible or not. However, the accuracy of existing white-box visibility metrics, such as HDR-VDP, is often not good enough. CNN-based black-box visibility metrics have proven to be more accurate, but they cannot account for differences in viewing conditions, such as display brightness and viewing distance.  In this paper, we propose a CNN-based visibility metric, which maintains the accuracy of deep network solutions and accounts for viewing conditions. To achieve this, we extend the existing dataset of locally visible differences (LocVis) with a new set of measurements, collected considering aforementioned viewing conditions. Then, we develop a hybrid model that combines white-box processing stages for modeling the effects of luminance masking and contrast sensitivity, with a black-box deep neural network.  We demonstrate that the novel hybrid model can handle the change of viewing conditions correctly and outperforms state-of-the-art metrics.