pwc-archive/papers-with-abstracts huggingface dataset:
Group Sampling for Scale Invariant Face Detection
Authors: Xiang Ming,  Fangyun Wei,  Ting Zhang,  Dong Chen,  Fang Wen
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Detection

Detectors based on deep learning tend to detect multi-scale faces on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that such multi-layer prediction is not necessary. Faces at all scales can be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting face detection across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only the last layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles.
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation
Authors: Yong Zhang,  Baoyuan Wu,  Weiming Dong,  Zhifeng Li,  Wei Liu,  Bao-Gang Hu,  Qiang Ji
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

Facial action unit (AU) intensity is an index to characterize human expressions. Accurate AU intensity estimation depends on three major elements: image representation, intensity estimator, and supervisory information. Most existing methods learn intensity estimator with fixed image representation, and rely on the availability of fully annotated supervisory information. In this paper, a novel general framework for AU intensity estimation is presented, which differs from traditional estimation methods in two aspects. First, rather than keeping image representation fixed, it simultaneously learns representation and intensity estimator to achieve an optimal solution. Second, it allows incorporating weak supervisory training signal from human knowledge (e.g. feature smoothness, label smoothness, label ranking, and positive label), which makes our model trainable even fully annotated information is not available. More specifically, human knowledge is represented as either soft or hard constraints which are encoded as regularization terms or equality/inequality constraints, respectively. On top of our novel framework, we additionally propose an efficient algorithm for optimization based on Alternating Direction Method of Multipliers (ADMM). Evaluations on two benchmark databases show that our method outperforms competing methods under different ratios of AU intensity annotations, especially for small ratios.
Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks
Authors: Meilu Zhu,  Daming Shi,  Mingjie Zheng,  Muhammad Sadiq
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Alignment, Facial Landmark Detection

In this paper, we present a simple and effective framework called Occlusion-adaptive Deep Networks (ODN) with the purpose of solving the occlusion problem for facial landmark detection. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module that can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, we propose a geometry-aware module to excavate geometric relationships between different facial components. Depending on the synergistic effect of three modules, the proposed network achieves better performance in comparison to state-of-the-art methods on challenging benchmark datasets.
Face Anti-Spoofing: Model Matters, so Does Data
Authors: Xiao Yang,  Wenhan Luo,  Linchao Bao,  Yuan Gao,  Dihong Gong,  Shibao Zheng,  Zhifeng Li,  Wei Liu
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Anti-Spoofing, Face Detection, model

Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. Previous approaches build models on datasets which do not simulate the real-world data well (e.g., small scale, insignificant variance, etc.). Existing models may rely on auxiliary information, which prevents these anti-spoofing solutions from generalizing well in practice. In this paper, we present a data collection solution along with a data synthesis technique to simulate digital medium-based face spoofing attacks, which can easily help us obtain a large amount of training data well reflecting the real-world scenarios. Through exploiting a novel Spatio-Temporal Anti-Spoof Network (STASN), we are able to push the performance on public face anti-spoofing datasets over state-of-the-art methods by a large margin. Since the proposed model can automatically attend to discriminative regions, it makes analyzing the behaviors of the network possible.We conduct extensive experiments and show that the proposed model can distinguish spoof faces by extracting features from a variety of regions to seek out subtle evidences such as borders, moire patterns, reflection artifacts, etc.
Progressive Teacher-Student Learning for Early Action Prediction
Authors: Xionghui Wang,  Jian-Fang Hu,  Jian-Huang Lai,  Jianguo Zhang,  Wei-Shi Zheng
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Early Action Prediction, Prediction, Temporal Action Localization

The goal of early action prediction is to recognize actions from partially observed videos with incomplete action executions, which is quite different from action recognition. Predicting early actions is very challenging since the partially observed videos do not contain enough action information for recognition. In this paper, we aim at improving early action prediction by proposing a novel teacher-student learning framework. Our framework involves a teacher model for recognizing actions from full videos, a student model for predicting early actions from partial videos, and a teacher-student learning block for distilling progressive knowledge from teacher to student, crossing different tasks. Extensive experiments on three public action datasets show that the proposed progressive teacher-student learning framework can consistently improve performance of early action prediction model. We have also reported the state-of-the-art performances for early action prediction on all of these sets.
Social Relation Recognition From Videos via Multi-Scale Spatial-Temporal Reasoning
Authors: Xinchen Liu,  Wu Liu,  Meng Zhang,  Jingwen Chen,  Lianli Gao,  Chenggang Yan,  Tao Mei
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Relation

Discovering social relations, e.g., kinship, friendship, etc., from visual contents can make machines better interpret the behaviors and emotions of human beings. Existing studies mainly focus on recognizing social relations from still images while neglecting another important media--video. On one hand, the actions and storylines in videos provide more important cues for social relation recognition. On the other hand, the key persons may appear at arbitrary spatial-temporal locations, even not in one same image from beginning to the end. To overcome these challenges, we propose a Multi-scale Spatial-Temporal Reasoning (MSTR) framework to recognize social relations from videos. For the spatial representation, we not only adopt a temporal segment network to learn global action and scene information, but also design a Triple Graphs model to capture visual relations between persons and objects. For the temporal domain, we propose a Pyramid Graph Convolutional Network to perform temporal reasoning with multi-scale receptive fields, which can obtain both long-term and short-term storylines in videos. By this means, MSTR can comprehensively explore the multi-scale actions and storylines in spatial-temporal dimensions for social relation reasoning in videos. Extensive experiments on a new large-scale Video Social Relation dataset demonstrate the effectiveness of the proposed framework.
Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification
Authors: Qize Yang,  Hong-Xing Yu,  Ancong Wu,  Wei-Shi Zheng
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Person Re-Identification, Unsupervised Person Re-Identification

While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn discriminative feature from patches instead of the whole images. The patch-based learning leverages similarity between patches to learn a discriminative model. Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches. To provide effective guidance for the PatchNet to learn discriminative patch feature on unlabeled datasets, we propose an unsupervised patch-based discriminative feature learning loss. In addition, we design an image-level feature learning loss to leverage all the patch features of the same image to serve as an image-level guidance for the PatchNet. Extensive experiments validate the superiority of our method for unsupervised person re-id. Our code is available at https://github.com/QizeYang/PAUL.
Polarimetric Camera Calibration Using an LCD Monitor
Authors: Zhixiang Wang,  Yinqiang Zheng,  Yung-Yu Chuang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camera Calibration

It is crucial for polarimetric imaging to accurately calibrate the polarizer angles and the camera response function (CRF) of a polarizing camera. When this polarizing camera is used in a setting of multiview geometric imaging, it is often required to calibrate its intrinsic and extrinsic parameters as well, for which Zhang's calibration method is the most widely used with either a physical checker board, or more conveniently a virtual checker pattern displayed on a monitor. In this paper, we propose to jointly calibrate the polarizer angles and the inverse CRF (ICRF) using a slightly adapted checker pattern displayed on a liquid crystal display (LCD) monitor. Thanks to the lighting principles and the industry standards of the LCD monitors, the polarimetric and radiometric calibration can be significantly simplified, when assisted by the extrinsic parameters estimated from the checker pattern. We present a simple linear method for polarizer angle calibration and a convex method for radiometric calibration, both of which can be jointly refined in a process similar to bundle adjustment. Experiments have verified the feasibility and accuracy of the proposed calibration method.
Zoom to Learn, Learn to Zoom
Authors: Xuaner Zhang,  Qifeng Chen,  Ren Ng,  Vladlen Koltun
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Super-Resolution

This paper shows that when applying machine learning to digital zoom, it is beneficial to operate on real, RAW sensor data. Existing learning-based super-resolution methods do not use real sensor data, instead operating on processed RGB images. We show that these approaches forfeit detail and accuracy that can be gained by operating on raw data, particularly when zooming in on distant objects. The key barrier to using real sensor data for training is that ground-truth high-resolution imagery is missing. We show how to obtain such ground-truth data via optical zoom and contribute a dataset, SR-RAW, for real-world computational zoom. We use SR-RAW to train a deep network with a novel contextual bilateral loss that is robust to mild misalignment between input and outputs images. The trained network achieves state-of-the-art performance in 4X and 8X computational zoom. We also show that synthesizing sensor data by resampling high-resolution RGB images is an oversimplified approximation of real sensor data and noise, resulting in worse image quality.
Single Image Reflection Removal Beyond Linearity
Authors: Qiang Wen,  Yinjie Tan,  Jing Qin,  Wenxi Liu,  Guoqiang Han,  Shengfeng He
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Reflection Removal

Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from two aspects. First, instead of synthesizing reflection with a fixed combination factor or kernel, we propose to synthesize reflection images by predicting a non-linear alpha blending mask. This enables a free combination of different blurry kernels, leading to a controllable and diverse reflection synthesis. Second, we design a cascaded network for reflection removal with three tasks: predicting the transmission layer, reflection layer, and the non-linear alpha blending mask. The former two tasks are the fundamental outputs, while the latter one being the side output of the network. This side output, on the other hand, making the training a closed loop, so that the separated transmission and reflection layers can be recombined together for training with a reconstruction loss. Extensive quantitative and qualitative experiments demonstrate the proposed synthesis and removal approaches outperforms state-of-the-art methods on two standard benchmarks, as well as in real-world scenarios.
Shape Unicode: A Unified Shape Representation
Authors: Sanjeev Muralikrishnan,  Vladimir G. Kim,  Matthew Fisher,  Siddhartha Chaudhuri
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Retrieval

3D shapes come in varied representations from a set of points to a set of images, each capturing different aspects of the shape. We propose a unified code for 3D shapes, dubbed Shape Unicode, that imbibes shape cues across these representations into a single code, and a novel framework to learn such a code space for any 3D shape dataset. We discuss this framework as a single go-to training model for any input representation, and demonstrate the effectiveness of the learned code space by applying it directly to common shape analysis tasks -- discriminative and generative. In this work, we use three common representations -- voxel grids, point clouds and multi-view projections -- and combine them into a single code. Note that while we use all three representations at training time, the code can be derived from any single representation during testing. We evaluate this code space on shape retrieval, segmentation and correspondence, and show that the unified code performs better than the individual representations themselves. Additionally, this code space compares quite well to the representation-specific state-of-the-art in these tasks. We also qualitatively discuss linear interpolation between points in this space, by synthesizing from intermediate points.
Robust Video Stabilization by Optimization in CNN Weight Space
Authors: Jiyang Yu,  Ravi Ramamoorthi
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation, Video Stabilization

We propose a novel robust video stabilization method. Unlike traditional video stabilization techniques that involve complex motion models, we directly model the appearance change of the frames as the dense optical flow field of consecutive frames. We introduce a new formulation of the video stabilization task based on first principles, which leads to a large scale non-convex problem. This problem is hard to solve, so previous optical flow based approaches have resorted to heuristics. In this paper, we propose a novel optimization routine that transfers this problem into the convolutional neural network parameter domain. While we exploit the general benefits of CNNs, including standard gradient-based optimization techniques, our method is a new approach to using CNNs purely as an optimizer rather than learning from data.Our method trains the CNN from scratch on each specific input example, and intentionally overfits the CNN parameters to produce the best result on the input example. By solving the problem in the CNN weight space rather than directly for image pixels, we make it a viable formulation for video stabilization. Our method produces both visually and quantitatively better results than previous work, and is robust in situations acknowledged as limitations in current state-of-the-art methods.
Learning Linear Transformations for Fast Image and Video Style Transfer
Authors: Xueting Li,  Sifei Liu,  Jan Kautz,  Ming-Hsuan Yang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Style Transfer, Video Style Transfer

Given a random pair of images, a universal style transfer method extracts the feel from a reference image to synthesize an output based on the look of a content image. Recent algorithms based on second-order statistics, however, are either computationally expensive or prone to generate artifacts due to the trade-off between image quality and runtime performance. In this work, we present an approach for universal style transfer that learns the transformation matrix in a data-driven fashion. Our algorithm is efficient yet flexible to transfer different levels of styles with the same auto-encoder network. It also produces stable video style transfer results due to the preservation of the content affinity. In addition, we propose a linear propagation module to enable a feed-forward network for photo-realistic style transfer. We demonstrate the effectiveness of our approach on three tasks: artistic style, photo-realistic and video style transfer, with comparisons to state-of-the-art methods.
Local Detection of Stereo Occlusion Boundaries
Authors: Jialiang Wang,  Todd Zickler
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Stereo Matching, Stereo Matching Hand

Stereo occlusion boundaries are one-dimensional structures in the visual field that separate foreground regions of a scene that are visible to both eyes (binocular regions) from background regions of a scene that are visible to only one eye (monocular regions). Stereo occlusion boundaries often coincide with object boundaries, and localizing them is useful for tasks like grasping, manipulation, and navigation. This paper describes the local signatures for stereo occlusion boundaries that exist in a stereo cost volume, and it introduces a local detector for them based on a simple feedforward network with relatively small receptive fields. The local detector produces better boundaries than many other stereo methods, even without incorporating explicit stereo matching, top-down contextual cues, or single-image boundary cues based on texture and intensity.
Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections
Authors: Hongyun Gao,  Xin Tao,  Xiaoyong Shen,  Jiaya Jia
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Image Deblurring

Dynamic Scene deblurring is a challenging low-level vision task where spatially variant blur is caused by many factors, e.g., camera shake and object motion. Recent study has made significant progress. Compared with the parameter independence scheme [19] and parameter sharing scheme [33], we develop the general principle for constraining the deblurring network structure by proposing the generic and effective selective sharing scheme. Inside the subnetwork of each scale, we propose a nested skip connection structure for the nonlinear transformation modules to replace stacked convolution layers or residual blocks. Besides, we build a new large dataset of blurred/sharp image pairs towards better restoration quality. Comprehensive experimental results show that our parameter selective sharing scheme, nested skip connection structure, and the new dataset are all significant to set a new state-of-the-art in dynamic scene deblurring.
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
Authors: Jia-Xing Zhao,  Yang Cao,  Deng-Ping Fan,  Ming-Ming Cheng,  Xuan-Yi Li,  Le Zhang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.
Compressing Convolutional Neural Networks via Factorized Convolutional Filters
Authors: Tuanhui Li,  Baoyuan Wu,  Yujiu Yang,  Yanbo Fan,  Yong Zhang,  Wei Liu
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Model Compression

This work studies the model compression for deep convolutional neural networks (CNNs) via filter pruning. The workflow of a traditional pruning consists of three sequential stages: pre-training the original model, selecting the pre-trained filters via ranking according to a manually designed criterion (e.g., the norm of filters), and learning the remained filters via fine-tuning. Most existing works follow this pipeline and focus on designing different ranking criteria for filter selection. However, it is difficult to control the performance due to the separation of filter selection and filter learning. In this work, we propose to conduct filter selection and filter learning simultaneously, in a unified model. To this end, we define a factorized convolutional filter (FCF), consisting of a standard real-valued convolutional filter and a binary scalar, as well as a dot-product operator between them. We train a CNN model with factorized convolutional filters (CNN-FCF) by updating the standard filter using back-propagation, while updating the binary scalar using the alternating direction method of multipliers (ADMM) based optimization method. With this trained CNN-FCF model, we only keep the standard filters corresponding to the 1-valued scalars, while all other filters and all binary scalars are discarded, to obtain a compact CNN model. Extensive experiments on CIFAR-10 and ImageNet demonstrate the superiority of the proposed method over state-of-the-art filter pruning methods.