mtybilly/Instruct2DS huggingface dataset:
Kernelized Subspace Pooling for Deep Local Descriptors
Authors: Xing Wei, Yue Zhang, Yihong Gong, Nanning Zheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Representing local image patches in an invariant and discriminative manner is an active research topic in computer vision. It has recently been demonstrated that local feature learning based on deep Convolutional Neural Network (CNN) can significantly improve the matching performance. Previous works on learning such descriptors have focused on developing various loss functions, regularizations and data mining strategies to learn discriminative CNN representations. Such methods, however, have little analysis on how to increase geometric invariance of their generated descriptors. In this paper, we propose a descriptor that has both highly invariant and discriminative power. The abilities come from a novel pooling method, dubbed Subspace Pooling (SP) which is invariant to a range of geometric deformations. To further increase the discriminative power of our descriptor, we propose a simple distance kernel integrated to the marginal triplet loss that helps to focus on hard examples in CNN training. Finally, we show that by combining SP with the projection distance metric, the generated feature descriptor is equivalent to that of the Bilinear CNN model, but outperforms the latter with much lower memory and computation consumptions. The proposed method is simple, easy to understand and achieves good performance. Experimental results on several patch matching benchmarks show that our method outperforms the state-of-the-arts significantly.
pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment
Authors: Je Hyeong Hong, Christopher Zach
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Bundle adjustment is a nonlinear refinement method for camera poses and 3D structure requiring sufficiently good initialization. In recent years, it was experimentally observed that useful minima can be reached even from arbitrary initialization for affine bundle adjustment problems (and fixed-rank matrix factorization instances in general). The key success factor lies in the use of the variable projection (VarPro) method, which is known to have a wide basin of convergence for such problems. In this paper, we propose the Pseudo Object Space Error (pOSE), which is an objective with cameras represented as a hybrid between the affine and projective models. This formulation allows us to obtain 3D reconstructions that are close to the true projective reconstructions while retaining a bilinear problem structure suitable for the VarPro method. Experimental results show that using pOSE has a high success rate to yield faithful 3D reconstructions from random initializations, taking one step towards initialization-free structure from motion.
Deformable Shape Completion With Graph Convolutional Autoencoders
Authors: Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learningbased method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality.
Learning From Millions of 3D Scans for Large-Scale 3D Face Recognition
Authors: Syed Zulqarnain Gilani, Ajmal Mian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles
Authors: N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Despite significant research in the area, reconstruction of multiple dynamic rigid objects (eg. vehicles) observed from   wide-baseline, uncalibrated and unsynchronized cameras, remains hard. On one hand, feature tracking works well within each view but is hard to correspond across multiple cameras with limited overlap in fields of view or due to occlusions. On the other hand, advances in deep learning have resulted in strong detectors that work across different viewpoints but are still not precise enough for triangulation-based reconstruction. In this work, we develop a framework to fuse both the single-view feature tracks and multi-view detected part locations to significantly improve the detection, localization and reconstruction of moving vehicles, even in the presence of strong occlusions. We demonstrate our framework at a busy traffic intersection by reconstructing over 62 vehicles passing within a 3-minute window. We evaluate the different components within our framework and compare to alternate  approaches such as reconstruction using tracking-by-detection.
Deep Material-Aware Cross-Spectral Stereo Matching
Authors: Tiancheng Zhi, Bernardo R. Pires, Martial Hebert, Srinivasa G. Narasimhan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Cross-spectral imaging provides strong benefits for recognition and detection tasks. Often, multiple cameras are used for cross-spectral imaging, thus requiring image alignment, or disparity estimation in a stereo setting. Increasingly, multi-camera cross-spectral systems are embedded in active RGBD devices (e.g. RGB-NIR cameras in Kinect and iPhone X). Hence, stereo matching also provides an opportunity to obtain depth without an active projector source. However, matching images from different spectral bands is challenging because of large appearance variations. We develop a novel deep learning framework to simultaneously transform images across spectral bands and estimate disparity. A material-aware loss function is incorporated within the disparity prediction network to handle regions with unreliable matching such as light sources, glass windshields and glossy surfaces. No depth supervision is required by our method. To evaluate our method, we used a vehicle-mounted RGB-NIR stereo system to collect 13.7 hours of video data across a range of areas in and around a city. Experiments show that our method achieves strong performance and reaches real-time speed.
Augmenting Crowd-Sourced 3D Reconstructions Using Semantic Detections
Authors: True Price, Johannes L. Schönberger, Zhen Wei, Marc Pollefeys, Jan-Michael Frahm
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Image-based 3D reconstruction for Internet photo collections has become a robust technology to produce impressive virtual representations of real-world scenes. However, several fundamental challenges remain for Structure-from-Motion (SfM) pipelines, namely: the placement and reconstruction of transient objects only observed in single views, estimating the absolute scale of the scene, and (suprisingly often) recovering ground surfaces in the scene. We propose a method to jointly address these remaining open problems of SfM. In particular, we focus on detecting people in individual images and accurately placing them into an existing 3D model. As part of this placement, our method also estimates the absolute scale of the scene from object semantics, which in this case constitutes the height distribution of the population. Further, we obtain a smooth approximation of the ground surface and recover the gravity vector of the scene directly from the individual person detections. We demonstrate the results of our approach on a number of unordered Internet photo collections, and we quantitatively evaluate the obtained absolute scene scales.
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
Authors: Stephan R. Richter, Stefan Roth
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction problem. To that end, we first develop a simple baseline network that predicts entire voxel tubes at each pixel of a reference view. By leveraging well-proven architectures for 2D pixel-prediction tasks, we attain state-of-the-art results, clearly outperforming purely voxel-based approaches. We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll. This allows reconstructing highly detailed shapes with complex topology, as demonstrated in extensive experiments; we clearly outperform previous octree-based approaches despite having a much simpler architecture using standard network components. Our Matryoshka networks further enable reconstructing shapes from IDs or shape similarity, as well as shape sampling.
Triplet-Center Loss for Multi-View 3D Object Retrieval
Authors: Xinwei He, Yang Zhou, Zhichao Zhou, Song Bai, Xiang Bai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Most existing 3D object recognition algorithms  focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data,  while learning discriminative features with deep metric learning for 3D object retrieval  is more or less neglected. In the paper,  we  study variants of deep metric learning losses for  3D object retrieval, which did not receive enough attention from this area. First , two kinds of representative losses, triplet loss and center loss,  are introduced which could  learn more discriminative features than traditional classification loss. Then we propose a novel loss named triplet-center loss, which can further enhance the discriminative power of the features. The proposed triplet-center loss learns a center for each class and requires that the distances between samples and centers from the same class are closer than those from different classes. Extensive experimental results on two popular 3D object retrieval benchmarks and two widely-adopted sketch-based 3D shape retrieval benchmarks consistently demonstrate the effectiveness of our proposed loss, and significant improvements have been achieved compared to the state-of-the-arts.
Learning 3D Shape Completion From Laser Scan Data With Weak Supervision
Authors: David Stutz, Andreas Geiger
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: 3D shape completion from partial point clouds is a fundamental problem in computer vision and computer graphics. Recent approaches can be characterized as either data-driven or learning-based. Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations. Learning-based approaches, in contrast, avoid the expensive optimization step and instead directly predict the complete shape from the incomplete observations using deep neural networks. However, full supervision is required which is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. Tackling 3D shape completion of cars on ShapeNet and KITTI, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and a state-of-the-art data-driven approach while being significantly faster. On ModelNet, we additionally show that the approach is able to generalize to other object categories as well.
End-to-End Learning of Keypoint Detector and Descriptor for Pose Invariant 3D Matching
Authors: Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, Jan Ernst, Jana Košecká
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Finding correspondences between images or 3D scans is at the heart of many computer vision and image retrieval applications and is often enabled by matching local keypoint descriptors. Various learning approaches have been applied in the past to different stages of the matching pipeline, considering detection, description, or metric learning objectives. These objectives were typically addressed separately and most previous work has focused on image data.  This paper proposes an end-to-end learning framework for keypoint detection and its representation (descriptor) for 3D depth maps or 3D scans, where the two can be jointly optimized towards task-specific objectives without a need for separate annotations. We employ a Siamese architecture augmented by a sampling layer and a novel score loss function which in turn affects the selection of region proposals. The positive and negative examples are obtained automatically by sampling corresponding region proposals based on their consistency with known 3D pose labels. Matching experiments with depth data on multiple benchmark datasets demonstrate the efficacy of the proposed approach, showing significant improvements over state-of-the-art methods.
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM
Authors: Haomin Liu, Mingyu Chen, Guofeng Zhang, Hujun Bao, Yingze Bao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Modern visual-inertial SLAM (VI-SLAM) achieves higher accuracy and robustness than pure visual SLAM, thanks to the complementariness of visual features and inertial measurements. However, jointly using visual and inertial measurements to optimize SLAM objective functions is a problem of high computational complexity. In many VI-SLAM applications, the conventional optimization solvers can only use a very limited number of recent measurements for real time pose estimation, at the cost of suboptimal localization accuracy. In this work, we renovate the numerical solver for VI-SLAM. Compared to conventional solvers, our proposal provides an exact solution with significantly higher computational efficiency. Our solver allows us to use remarkably larger number of measurements to achieve higher accuracy and robustness. Furthermore, our method resolves the global consistency problem that is unaddressed by many state-of-the-art SLAM systems: to guarantee the minimization of re-projection function and inertial constraint function during loop closure. Experiments demonstrate our novel formulation renders lower localization error and more than 10x speedup compared to alternatives. We release the source code of our implementation to benefit the community.
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Authors: Zhichao Yin, Jianping Shi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.
Radially-Distorted Conjugate Translations
Authors: James Pritts, Zuzana Kukelova, Viktor Larsson, Ondřej Chum
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated by the Gr{\"o}bner-basis method are stable, small and fast. The proposed solvers are used in a RANSAC-based estimator. Rectification and lens distortion are recovered from either one conjugately translated affine-covariant feature or two independently translated similarity-covariant features. Experiments confirm that RANSAC accurately estimates the rectification and radial distortion with very few iterations. The proposed solvers are evaluated against the state-of-the-art for affine rectification and radial distortion estimation.
Deep Ordinal Regression Network for Monocular Depth Estimation
Authors: Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Dacheng Tao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed prob- lem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-resolution feature maps. To obtain high-resolution depth maps, skip-connections or multi- layer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multi-scale network structure which avoids unnecessary spatial pooling and captures multi-scale information in parallel. The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.
Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras
Authors: Pedro Miraldo, Francisco Eiras, Srikumar Ramalingam
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries. A vanishing point is a point on the image space where parallel lines in 3D space appear to converge, whereas a vanishing line passes through 2 or more vanishing points. While such concepts are simple and intuitive in perspective cameras, their counterparts in catadioptric cameras (obtained using mirrors and lenses) are more involved. For example, lines in the 3D space map to higher degree curves in catadioptric cameras. The projection of a set of 3D parallel lines converges on a single point in perspective images, whereas they converge to more than one point in catadioptric cameras. To the best of our knowledge, we are not aware of any systematic development of analytical models for vanishing points and vanishing curves in different types of catadioptric cameras. In this paper, we derive parametric equations for vanishing points and vanishing curves using the calibration parameters, mirror shape coefficients, and direction vectors of parallel lines in 3D space. We show compelling experimental results on vanishing point estimation and absolute pose estimation for a wide variety of catadioptric cameras in both simulations and real experiments.
Learning Depth From Monocular Videos Using Direct Methods
Authors: Chaoyang Wang, José Miguel Buenaposada, Rui Zhu, Simon Lucey
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo. In previous works, separate pose and depth CNN predictors had to be determined such that their joint outputs minimized the photometric error. Inspired by recent advances in direct visual odometry (DVO), we argue that the depth CNN predictor can be learned without a pose CNN predictor. Further, we demonstrate empirically that incorporation of a differentiable implementation of DVO, along with a novel depth normalization strategy - substantially improves performance over state of the art that use monocular videos for training.
Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display
Authors: Shizheng Wang, Wenjuan Liao, Phil Surman, Zhigang Tu, Yuanjin Zheng, Junsong Yuan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Multi-layer light field displays are a type of computational three-dimensional (3D) display which has recently gained increasing interest for its holographic-like effect and natural compatibility with 2D displays. However, the major shortcoming, depth limitation, still cannot be overcome in the traditional light field modeling and reconstruction based on multi-layer liquid crystal displays (LCDs). Considering this disadvantage, our paper incorporates a salience guided depth optimization over a limited display range to calibrate the displayed depth and present the maximum area of salience region for multi-layer light field display. Different from previously reported cascaded light field displays that use the fixed initialization plane as the depth center of display content, our method automatically calibrates the depth initialization based on the salience results derived from the proposed contrast enhanced salience detection method. Experiments demonstrate that the proposed method provides a promising advantage in visual perception for the compressive light field displays from both software simulation and prototype demonstration.
MegaDepth: Learning Single-View Depth Prediction From Internet Photos
Authors: Zhengqi Li, Noah Snavely
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Single-view depth prediction is a fundamental problem in computer vision. Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data. Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI). We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea. Data derived from MVS comes with its own challenges, including noise and unreconstructable objects. We address these challenges with new data cleaning methods, as well as automatically augmenting our data with ordinal depth relations generated using semantic segmentation. We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization—not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training.
LayoutNet: Reconstructing the 3D Room Layout From a Single RGB Image
Authors: Chuhang Zou, Alex Colburn, Qi Shan, Derek Hoiem
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. "L"-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works.  Our network architecture is similar to that of RoomNet, but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Authors: Konstantinos Batsos, Changjiang Cai, Philippos Mordohai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions. Many of these assumptions, however, had been validated extensively and hold for the majority of possible inputs. In this paper, we generate a matching volume leveraging both data with ground truth and conventional wisdom. We accomplish this by coalescing diverse evidence from a bidirectional matching process via random forest classifiers. We show that the resulting matching volume estimation method achieves similar accuracy to purely data-driven alternatives on benchmarks and that it  generalizes to unseen data much better. In fact, the results we submitted to the KITTI benchmarks were generated using a classifier trained on the Middlebury dataset.
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
Authors: Jiahao Pang, Wenxiu Sun, Chengxi Yang, Jimmy Ren, Ruichao Xiao, Jin Zeng, Liang Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major difficulty is to collect accurate ground-truth disparities for stereo pairs in the target domain. In this work, we propose a self-adaptation approach for CNN training, utilizing both synthetic training data (with ground-truth disparities) and stereo pairs in the new domain (without ground-truths). Our method is driven by two empirical observations. By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details. To avoid i) while exploiting ii), we formulate an iterative optimization problem with graph Laplacian regularization. At each iteration, the CNN adapts itself better to the new domain: we let the CNN learn its own higher-resolution output; at the meanwhile, a graph Laplacian regularization is imposed to discriminatively keep the desired edges while smoothing out the artifacts. We demonstrate the effectiveness of our method in two domains: daily scenes collected by smartphone cameras, and street views captured in a driving car.
Exploring Disentangled Feature Representation Beyond Face Identification
Authors: Yu Liu, Fangyin Wei, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper proposes learning disentangled but complementary face features with a minimal supervision by face identification. Specifically, we construct an identity Distilling and Dispelling Auto-Encoder (D^2AE) framework that adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool the verification system. Thanks to the design of two-stream cues, the learned disentangled features represent not only the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only preserve state-of-the-art identity verification performance on LFW, but also acquire comparable discriminative power for face attribute recognition on CelebA and LFWA. Moreover, the proposed system is ready to semantically control the face generation/editing based on various identities and attributes in an unsupervised manner.
Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering
Authors: Kaili Zhao, Wen-Sheng Chu, Aleix M. Martinez
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a scalable weakly supervised clustering approach to learn facial action units (AUs) from large, freely available web images. Unlike most existing methods (e.g., CNNs) that rely on fully annotated data, our method exploits web images with inaccurate annotations. Specifically, we derive a weakly-supervised spectral algorithm that learns an embedding space to couple image appearance and semantics. The algorithm has efficient gradient update, and scales up to large quantities of images with a stochastic extension. With the learned embedding space, we adopt rank-order clustering to identify groups of visually and semantically similar images, and re-annotate these groups for training AU classifiers. Evaluation on the 1 millon EmotioNet dataset demonstrates the effectiveness of our approach: (1) our learned annotations reach on average 91.3% agreement with human annotations on 7 common AUs, (2) classifiers trained with re-annotated images perform comparably to, sometimes even better than, its supervised CNN-based counterpart, and (3) our method offers intuitive outlier/noise pruning instead of forcing one annotation to every image. Code is available.
Human Pose Estimation With Parsing Induced Learner
Authors: Xuecheng Nie, Jiashi Feng, Yiming Zuo, Shuicheng Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Human pose estimation still faces various difficulties in challenging scenarios. Human parsing, as a closely related task, can provide valuable cues for better pose estimation, which however has not been fully exploited. In this paper, we propose a novel Parsing Induced Learner to exploit parsing information to effectively assist pose estimation by learning to fast adapt the base pose estimation model. The proposed Parsing Induced Learner is composed of a parsing encoder and a pose model parameter adapter, which together learn to predict dynamic parameters of the pose model to extract complementary useful features for more accurate pose estimation. Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed  Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art. Cross-dataset experiments also show that the proposed Parsing Induced Learner from LIP dataset can accelerate learning of a human pose estimation model on MPII benchmark in addition to achieving outperforming performance.
Multi-Level Factorisation Net for Person Re-Identification
Authors: Xiaobin Chang, Timothy M. Hospedales, Tao Xiang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes. We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.
Attention-Aware Compositional Network for Person Re-Identification
Authors: Jing Xu, Rui Zhao, Feng Zhu, Huaming Wang, Wanli Ouyang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Person re-identification (ReID) is to identify pedestrians observed from different camera views based on visual appearance. It is a challenging task due to large pose variations, complex background clutters and severe occlusions. Recently, human pose estimation by predicting joint locations was largely improved in accuracy. It is reasonable to use pose estimation results for handling pose variations and background clutters, and such attempts have obtained great improvement in ReID performance. However, we argue that the pose information was not well utilized and hasn’t yet been fully exploited for person ReID. In this work, we introduce a novel framework called Attention-Aware Compositional Network (AACN) for person ReID. AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA is learned and applied to mask out undesirable background features in pedestrian feature maps. Furthermore, pose-guided visibility scores are estimated for body parts to deal with part occlusion in the proposed AFC module. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID, CUHK03-NP and DukeMTMC-reID.
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
Authors: Wayne Wu, Chen Qian, Shuo Yang, Quan Wang, Yici Cai, Qiang Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model are publicly available at https://wywu.github.io/projects/LAB/LAB.html
Demo2Vec: Reasoning Object Affordances From Online Videos
Authors: Kuan Fang, Te-Lin Wu, Daniel Yang, Silvio Savarese, Joseph J. Lim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of demonstration videos and predicts the interaction region and the action label on a target image of the same object. We introduce the Online Product Review dataset for Affordance (OPRA) by collecting and labeling  diverse YouTube product review videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset.
Monocular 3D Pose and Shape Estimation of Multiple People in Natural Scenes - The Importance of Multiple Scene Constraints
Authors: Andrei Zanfir, Elisabeta Marinoiu, Cristian Sminchisescu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Human sensing has greatly benefited from recent advances in deep learning, parametric human modeling, and large scale 2d and 3d datasets. However, existing 3d models make strong assumptions about the scene, considering either a single person per image, full views of the person, a simple background or many cameras. In this paper, we leverage state-of-the-art deep multi-task neural networks and parametric human and scene modeling, towards a fully automatic monocular visual sensing system for multiple interacting people, which (i) infers the 2d and 3d pose and shape of multiple people from a single image, relying on detailed semantic representations at both model and image level, to guide a combined optimization with feedforward and feedback components, (ii) automatically integrates scene constraints including ground plane support and simultaneous volume occupancy by multiple people, and (iii) extends the single image model to video by optimally solving the temporal person assignment problem and imposing coherent temporal pose and motion reconstructions while preserving image alignment fidelity. We perform experiments on both single and multi-person datasets, and systematically evaluate each component of the model, showing improved performance and extensive multiple human sensing capability. We also apply our method to images with multiple people, severe occlusions and diverse backgrounds captured in challenging natural scenes, and obtain results of good perceptual quality.
3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children With Autism
Authors: Elisabeta Marinoiu, Mihai Zanfir, Vlad Olaru, Cristian Sminchisescu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as  non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction.
Facial Expression Recognition by De-Expression Residue Learning
Authors: Huiyuan Yang, Umur Ciftci, Lijun Yin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: A facial expression is a combination of an expressive component and a neutral component of a person. In this paper, we propose to recognize facial expressions by extracting information of the expressive component through a de-expression learning procedure, called De-expression Residue Learning (DeRL). First, a generative model is trained by cGAN. This model generates the corresponding neutral face image for any input face image. We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers. Given the neutral face image, unlike previous works using pixel-level or feature-level difference for facial expression classification, our new method learns the deposition (or residue) that remains in the intermediate layers of the generative model. Such a residue is essential as it contains the expressive component deposited in the generative model from any input facial expression images. Seven public facial expression databases are employed in our experiments. With two databases (BU-4DFE and BP4D-spontaneous) for pre-training, the DeRL method has been evaluated on five databases, CK+, Oulu-CASIA, MMI, BU- 3DFE, and BP4D+. The experimental results demonstrate the superior performance of the proposed method.
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Authors: Yuanlu Xu, Lei Qin, Xiaobai Liu, Jianwen Xie, Song-Chun Zhu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.