mtybilly/Instruct2DS huggingface dataset:
Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
Authors: Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a material acquisition system that can recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our technique images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our network is trained using a large-scale SVBRDF dataset and designed to incorporate physical insights for material estimation, including an in-network rendering layer to model appearance and a material classification task to provide additional supervision during training. Finally, we refine the results from the network using a dense CRF module whose terms are designed specifically for our task. We demonstrate that our CNN-based SVBRDF inference leads to state-of-the-art results on a wide variety of materials on both synthetic and real data. We also provide extensive ablation studies to evaluate our network and demonstrate large improvements in comparisons with prior works.
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Authors: Yan Wang, Lingxi Xie, Siyuan Qiao, Ya Zhang, Wenjun Zhang, Alan L. Yuille
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to use spatial information. This paper addresses this issue by a recalibration process, which refers to the surrounding region of each neuron, computes an importance value and multiplies it to the original neural response. Our approach is named multi-scale spatially-asymmetric recalibration (MS-SAR), which, besides introducing spatial asymmetry into convolution, extracts visual cues from regions at multiple scales to allow richer information to be incorporated. MS-SAR is implemented in an efficient way, so that only small fractions of extra parameters and computations are required. We apply MS-SAR to several popular network architectures, in which all convolutional layers are recalibrated, and demonstrate superior performance in both CIFAR and ILSVRC2012 classification tasks.
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
Authors: Zhengming Ding, Sheng Li, Ming Shao, Yun Fu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches.
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
Authors: Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. This paper is in the vein of supervised video summarization using sequential determinantal point processes (SeqDPPs), which models diversity by a probabilistic distribution. We improve this model in two folds. In terms of learning, we propose a large-margin algorithm to address the exposure bias problem in SeqDPP. In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary. Moreover, we also significantly extend a popular video summarization dataset by 1) more egocentric videos, 2) dense user annotations, and 3) a refined evaluation scheme. We conduct extensive experiments on this dataset (about 60 hours of videos in total) and compare our approach to several competitive baselines.
Specular-to-Diffuse Translation for Multi-View Reconstruction
Authors: Shihao Wu, Hui Huang, Tiziano Portenier, Matan Sela, Daniel Cohen-Or, Ron Kimmel, Matthias Zwicker
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively. Our network extends unsupervised image-to-image translation to multi-view ``specular to diffuse" translation. To preserve object appearance across multiple views, we introduce a Multi-View Coherence loss (MVC) that evaluates the similarity and faithfulness of local patches after the view-transformation. Our MVC loss ensures that the similarity of local correspondences among multi-view images is preserved under the image-to-image translation. As a result, our network yields significantly better results than several single-view baseline techniques. In addition, we carefully design and generate a large synthetic training data set using physically-based rendering. During testing, our network takes only the raw glossy images as input, without extra information such as segmentation masks or lighting estimation. Results demonstrate that multi-view reconstruction can be significantly improved using the images filtered by our network. We also show promising performance on real world training and testing data.
RESOUND: Towards Action Recognition without Representation Bias
Authors: Yingwei Li, Yi Li, Nuno Vasconcelos
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: While large datasets have proven to be a key enabler for progress in computer vision, they can have biases that lead to erroneous conclusions. The notion of the representation bias of a dataset is proposed to combat this problem. It captures the fact that representations other than the ground-truth representation can achieve good performance on any given dataset. When this is the case, the dataset is said not to be well calibrated. Dataset calibration is shown to be a necessary condition for the standard state-of-the-art evaluation practice to converge to the ground-truth representation. A procedure, RESOUND, is proposed to quantify and minimize representation bias. Its application to the problem of action recognition shows that current datasets are biased towards static representations (objects, scenes and people). Two versions of RESOUND are studied. An Explicit RESOUND procedure is proposed to assemble new datasets by sampling existing datasets. An implicit RESOUND procedure is used to guide the creation of a new dataset, Diving48, of over 18,000 video clips of competitive diving actions, spanning 48 ﬁne-grained dive classes. Experimental evaluation conﬁrms the eﬀectiveness of RESOUND to reduce the static biases of current datasets.
A Framework for Evaluating 6-DOF Object Trackers
Authors: Mathieu Garon, Denis Laurendeau, Jean-Francois Lalonde
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a challenging and realistic novel dataset for evaluating 6-DOF object tracking algorithms. Existing datasets show serious limitations---notably, unrealistic synthetic data, or real data with large fiducial markers---preventing the community from obtaining an accurate picture of the state-of-the-art. Using a data acquisition pipeline based on a commercial motion capture system for acquiring accurate ground truth poses of real objects with respect to a Kinect V2 camera, we build a dataset which contains a total of 297 calibrated sequences. They are acquired in three different scenarios to evaluate the performance of trackers: stability, robustness to occlusion and accuracy during challenging interactions between a person and the object. We conduct an extensive study of a deep 6-DOF tracking architecture and determine a set of optimal parameters. We enhance the architecture and the training methodology to train a 6-DOF tracker that can robustly generalize to objects never seen during training, and demonstrate favorable performance compared to previous approaches trained specifically on the objects to track.
Extending Layered Models to 3D Motion
Authors: Dong Lao, Ganesh Sundaramoorthi
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera. Wegeneralize layered inference to the aforementioned case and correspond-ing self-occlusion phenomena. We accomplish this by introducing a flat-tened 3D object representation, which is a compact representation of anobject that contains all visible portions of the object seen in the video,including parts of an object that are self-occluded (as well as occluded)in one frame but seen in another. We formulate the inference of such flat-tened representations and motion segmentation, and derive an optimiza-tion scheme. We also introduce a new depth ordering scheme, which isindependent of layered inference and addresses the case of self-occlusion.It requires almost no computation given the flattened representations.Experiments on benchmark datasets show the advantage of our methodcompared to existing layered methods, which do not model 3D motionand self-occlusion.
Long-term Tracking in the Wild: a Benchmark
Authors: Jack Valmadre, Luca Bertinetto, Joao F. Henriques, Ran Tao, Andrea Vedaldi, Arnold W.M. Smeulders, Philip H.S. Torr, Efstratios Gavves
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this "short-term" scenario, which is poorly representative of practitioners' needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is oxuva.net.
Human Motion Analysis with Deep Metric Learning
Authors: Huseyin Coskun, David Joseph Tan, Sailesh Conjeti, Nassir Navab, Federico Tombari
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learning objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our experiments on two different datasets demonstrate significant improvements over conventional human motion metrics.
Adaptive Affinity Fields for Semantic Segmentation
Authors: Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu, Stella X. Yu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Existing semantic segmentation methods mostly rely on per-pixel supervision, unable to capture structural regularity present in natural images. Instead of learning to enforce semantic labels on individual pixels, we propose to enforce affinity field patterns in individual pixel neighbourhoods, i.e., the semantic label patterns of whether neighbour pixels are in the same segment should match between the prediction and the ground-truth. The affinity fields thus characterize the intrinsic geometric relationships inside a given scene, such as ``motorcycles have round wheels''. We further develop a novel method for learning the optimal neighbourhood size for each semantic category, with an adversarial loss that optimizes over worst-case scenarios. Unlike the popular Conditional Random Field approaches, our adaptive affinity field method has no extra parameters during inference, and is also less sensitive to input appearance changes. Extensive evaluations on Cityscapes, PASCAL VOC 2012 and GTA5 datasets demonstrate AAF provides an effective, efficient, and robust solution for semantic segmentation.
Hierarchy of Alternating Specialists for Scene Recognition
Authors: Hyo Jin Kim, Jan-Michael Frahm
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We introduce a method for improving convolutional neural networks (CNNs) for scene classification. We present a hierarchy of specialist networks, which disentangles the intra-class variation and inter-class similarity in a coarse to fine manner. Our key insight is that each subset within a class is often associated with different types of inter-class similarity. This suggests that existing network of experts approaches that organize classes into coarse categories are suboptimal. In contrast, we group images based on high-level appearance features rather than their class membership and dedicate a specialist model per group. In addition, we propose an alternating architecture with a global ordered- and a global orderless-representation to account for both the coarse layout of the scene and the transient objects. We demonstrate that it leads to better performance than using a single type of representation as well as the fused features. We also introduce a mini-batch soft k-means that allows end-to-end fine-tuning, as well as a novel routing function for assigning images to specialists. Experimental results show that the proposed approach achieves a significant improvement over baselines including the existing tree-structured CNNs with class-based grouping.
Multi-Scale Structure-Aware Network for Human Pose Estimation
Authors: Lipeng Ke, Ming-Ching Chang, Honggang Qi, Siwei Lyu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across scales, (2) multi-scale regression network at the end to globally optimize the structural matching of the multi-scale features, (3) structure-aware loss used in the intermediate supervision and at the regression to improve the matching of keypoints and respective neighbors to infer a higher-order matching configurations, and (4) a keypoint masking training scheme that can effectively fine-tune our network to robustly localize occluded keypoints via adjacent matches. Our method can effectively improve state-of-the-art pose estimation methods that suffer from difficulties in scale varieties, occlusions, and complex multi-person scenarios. This multi-scale supervision tightly integrates with the regression network to effectively (i) localize keypoints using the ensemble of multi-scale features, and (ii) infer global pose configuration by maximizing structural consistencies across multiple keypoints and scales. The keypoint masking training enhances these advantages to focus learning on hard occlusion samples. Our method achieves the leading position in the MPII challenge leaderboard among the state-of-the-art methods.
License Plate Detection and Recognition in Unconstrained Scenarios
Authors: Sergio Montazzolli Silva, Claudio Rosito Jung
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. European, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. Our main contribution is the introduction of a novel Convolutional Neural Network (CNN) capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition (OCR) method to obtain the final result. As an additional contribution, we also present manual annotations for a challenging set of LP images from different regions and acquisition conditions. Our experimental results indicate that the proposed method, without any parameter adaptation or fine tuning for a specific scenario, performs similarly to state-of-the-art commercial systems in traditional datasets, and outperforms both academic and commercial approaches in challenging datasets.
Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders
Authors: Panna Felsen, Patrick Lucey, Sujoy Ganguly
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e.g., autonomous vehicles, security, surveillance, sports). In this paper, we present a technique using conditional variational autoencoder which learns a model that "personalizes'' prediction to individual agent behavior within a group representation. Given the volume of data available and its adversarial nature, we focus on the sport of basketball and show that our approach efficiently predicts context-specific agent motions. We find that our model generates results that are three times as accurate as previous state of the art approaches (5.74 ft vs. 17.95 ft).
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
Authors: Guosheng Hu, Li Liu, Yang Yuan, Zehao Yu, Yang Hua, Zhihong Zhang, Fumin Shen, Ling Shao, Timothy Hospedales, Neil Robertson, Yongxin Yang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Facial expression recognition is a topical task. However, very little research investigates subtle expression recognition, which is important for mental activity analysis, deception detection, etc. We address subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (MTL) to effectively leverage a side task: facial landmark detection. Existing MTL methods follow a design pattern of shared bottom CNN layers and task-specific top layers. However, the sharing architecture is usually heuristically chosen, as it is difficult to decide which layers should be shared. Our approach is composed of (1) a novel MTL framework that automatically learns which layers to share through optimisation under tensor trace norm regularisation and (2) an invariant representation learning approach that allows the CNN to leverage tasks defined on disjoint datasets without suffering from dataset distribution shift. To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW). LSEMSW includes a variety of cognitive states as well as basic emotions. It contains 176K images, manually annotated with 13 emotions, and thus provides the first subtle expression dataset large enough for training deep CNNs. Evaluations on LSEMSW and 300-W (landmark) databases show the effectiveness of the proposed methods. In addition, we investigate transferring knowledge learned from LSEMSW database to traditional (non-subtle) expression recognition. We achieve very competitive performance on Oulu-Casia NIR&Vis and CK+ databases via transfer learning.
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
Authors: Yifei Shi, Kai Xu, Matthias Niessner, Szymon Rusinkiewicz, Thomas Funkhouser
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural net that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from other images. We train the network on 10 million triplets of coplanar and non-coplanar patches, and evaluate on a new coplanarity benchmark created from commodity RGB-D scans. Experiments show that our learned descriptor outperforms alternatives extended for this new task by a significant margin. In addition, we demonstrate the benefits of coplanarity matching in a robust RGBD reconstruction formulation. We find that coplanarity constraints detected with our method are sufficient to get reconstruction results comparable to state-of-the-art frameworks on most scenes, but outperform other methods on standard benchmarks when combined with a simple keypoint method.
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Authors: Haowen Deng, Tolga Birdal, Slobodan Ilic
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point-set sparsity, is end-to-end, fast, and can extract powerful rotation invariant descriptors. Thanks to a novel feature visualization, its evolution can be monitored to provide interpretable insights. Our extensive experiments demonstrate that despite having six degree-of-freedom invariance and lack of training labels, our network achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present. PPF-FoldNet achieves $9%$ higher recall on standard benchmarks, $23%$ higher recall when rotations are introduced into the same datasets and finally, a margin of $>35%$ is attained when point density is significantly decreased.
HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation
Authors: Yidan Zhou, Jian Lu, Kuo Du, Xiangbo Lin, Yi Sun, Xiaohong Ma
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: The goal of this paper is to estimate the 3D coordinates of the hand joints from a single depth image. To give consideration to both the accuracy and the real time performance, we design a novel three-branch Convolutional Neural Networks named Hand Branch Ensemble network (HBE), where the three branches correspond to the three parts of a hand: the thumb, the index finger and the other fingers. The structural design inspiration of the HBE network comes from the understanding of the differences in the functional importance of different fingers. In addition, a feature ensemble layer along with a low-dimensional embedding layer ensures the overall hand shape constraints. The experimental results on three public datasets demonstrate that our approach achieves comparable or better performance to state-of-the-art methods with less training data, shorter training time and faster frame rate.
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Authors: Oliver Groth, Fabian B. Fuchs, Ingmar Posner, Andrea Vedaldi
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Physical intuition is pivotal for intelligent agents to perform complex tasks. In this paper we investigate the passive acquisition of an intuitive understanding of physical principles as well as the active utilisation of this intuition in the context of generalised object stacking. To this end, we provide ShapeStacks: a simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. We train visual classifiers for binary stability prediction on the ShapeStacks data and scrutinise their learned physical intuition. Due to the richness of the training data our approach also generalises favourably to real-world scenarios achieving state-of-the-art stability prediction on a publicly available benchmark of block towers. We then leverage the physical intuition learned by our model to actively construct stable stacks and observe the emergence of an intuitive notion of stackability - an inherent object affordance - induced by the active stacking task. Our approach performs well exceeding the stack height observed during training and even manages to counterbalance initially unstable structures.
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification
Authors: Xing Wei, Yue Zhang, Yihong Gong, Jiawei Zhang, Nanning Zheng
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Designing discriminative and invariant features is the key to visual recognition. Recently, the bilinear pooled feature matrix of Convolutional Neural Network (CNN) has shown to achieve state-of-the-art performance on a range of fine-grained visual recognition tasks. The bilinear feature matrix collects second-order statistics and is closely related to the covariance matrix descriptor. However, the bilinear feature could suffer from the visual burstiness phenomenon similar to other visual representations such as VLAD and Fisher Vector. The reason is that the bilinear feature matrix is sensitive to the magnitudes and correlations of local CNN feature elements which can be measured by its singular values. On the other hand, the singular vectors are more invariant and reasonable to be adopted as the feature representation. Motivated by this point, we advocate an alternative pooling method which transforms the CNN feature matrix to an orthonormal matrix consists of its principal singular vectors. Geometrically, such orthonormal matrix lies on the Grassmann manifold, a Riemannian manifold whose points represent subspaces of the Euclidean space. Similarity measurement of images reduces to comparing the principal angles between these ``homogeneous" subspaces and thus is independent of the magnitudes and correlations of local CNN activations. In particular, we demonstrate that the projection distance on the Grassmann manifold deduces a bilinear feature mapping without explicitly computing the bilinear feature matrix, which enables a very compact feature and classifier representation. Experimental results show that our method achieves an excellent balance of model complexity and accuracy on a variety of fine-grained image classification datasets.
Deep Generative Models for Weakly-Supervised Multi-Label Classification
Authors: Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data. Since such annotation process is in general costly, we focus on the learning task of weakly-supervised multi-label classification (WS-MLC). In this paper, we tackle WS-MLC by learning deep generative models for describing the collected data. In particular, we introduce a sequential network architecture for constructing our generative model with the ability to approximate observed data posterior distributions. We show that how information of training data with missing labels or unlabeled ones can be exploited, which allows us to learn multi-label classifiers via scalable variational inferences. Empirical studies on various scales of datasets demonstrate the effectiveness of our proposed model, which performs favorably against state-of-the-art MLC algorithms.
SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation
Authors: Wenqiang Xu, Yonglu Li, Cewu Lu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quantities of training samples with very minor effort. Our pipeline is well-suited to scenes that can be scanned, i.e. most indoor and some outdoor scenarios. To evaluate our performance, we build three representative scenes and a new dataset, with 3D models of various common objects categories and annotated real-world scene images. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human labor cost.
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
Authors: Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a low-performing solver, the Max Product Linear Programming (MPLP) algorithm, we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRW-S) message-passing algorithm. Additionally, our solver is highly parallel, in contrast to TRW-S, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks, as well, as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
Authors: Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and their robust as well as accurate diffusion across time. We evaluate our proposed algorithm on the challenging DAVIS, SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge detector trained on 200 images, our method achieves state-of-the-art results outperforming deep learning based methods in the unsupervised setting. We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.
Semi-Supervised Deep Learning with Memory
Authors: Yanbei Chen, Xiatian Zhu, Shaogang Gong
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semi-supervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Authors: Liangliang Ren, Xin Yuan, Jiwen Lu, Ming Yang, Jie Zhou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking. In this paper, we propose a deep reinforcement learning with iterative shift (DRL-IS) method for single object tracking, where an actor-critic network is introduced to predict the iterative shifts of object bounding boxes, and evaluate the shifts to take actions on whether to update object models or re-initialize tracking. Since locating an object is achieved by an iterative shift process, rather than online classification on many sampled locations, the proposed method is robust to cope with large deformations and abrupt motion, and computationally efficient since finding a target takes up to 10 shifts. In offline training, the critic network guides to learn how to make decisions jointly on motion estimation and tracking status in an end-to-end manner. Experimental results on the OTB benchmarks with large deformation improve the tracking precision by 1.7% and runs about 5 times faster than the competing state-of-the-art methods.
X2Face: A network for controlling face generation using images, audio, and pose codes
Authors: Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but the pose and expression of the face in the driving frame. Second, we propose a method for training the network fully self-supervised using a large collection of video data. Third, we show that the generation process can be driven by other modalities, such as audio or pose codes, without any further training of the network. The generation results for driving a face with another face are compared to state-of-the-art self-supervised/supervised methods. We show that our approach is more robust than other methods, as it makes fewer assumptions about the input data. We also show examples of using our framework for video face editing.
Correcting the Triplet Selection Bias for Triplet Loss
Authors: Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, Dacheng Tao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet mining is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selected triplets, e.g., it almost does not converge with randomly selected triplets and selecting hardest triplets also leads to bad local minima. We argue that the bias in sampling of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet sampling by adaptively correcting the distribution shift on sampled triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method.
Women also Snowboard: Overcoming Bias in Captioning Models
Authors: Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, Anna Rohrbach
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data (e.g., if a word is present in 60% of training sentences, it might be predicted in 70% of sentences at test time). This can lead to incorrect captions in domains where unbiased captions are desired, or required, due to over-reliance on the learned prior and image context. In this work we investigate generation of gender-specific caption words (e.g. man, woman) based on the person's appearance or the image context. We introduce a new Equalizer model that encourages equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present. The resulting model is forced to look at a person rather than use contextual cues to make a gender-specific prediction. The losses that comprise our model, the Appearance Confusion Loss and the Confident Loss, are general, and can be added to any description model in order to mitigate impacts of unwanted bias in a description dataset. Our proposed model has lower error than prior work when describing images with people and mentioning their gender and more closely matches the ground truth ratio of sentences including women to sentences including men. Finally, we show that our model more often looks at people when predicting their gender.
GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction
Authors: Li Jiang, Shaoshuai Shi, Xiaojuan Qi, Jiaya Jia
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single view image. Distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point loss does not constrain the 3D model from a global perspective. We propose to add geometric adversarial loss (GAL). It is composed of two terms where the geometric loss ensures consistent shape of reconstructed 3D models close to ground-truth from different viewpoints, and the conditional adversarial loss generates a semantically-meaningful point cloud. GAL benefits predicting the obscured part of objects and maintaining geometric structure of the predicted 3D model. Both the qualitative results and quantitative analysis manifest the generality and suitability of our method.
Contextual-based Image Inpainting: Infer, Match, and Translate
Authors: Yuhang Song, Chao Yang, Zhe Lin, Xiaofeng Liu, Qin Huang, Hao Li, C.-C. Jay Kuo
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.
Inner Space Preserving Generative Pose Machine
Authors: Shuangjun Liu, Sarah Ostadabbas
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image ``inner space'' preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
Authors: Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects on large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfaction. The reason is that small objects lack sufficient detailed appearance information, which can distinguish them from the background or similar objects. To deal with small object detection problem, we propose an end-to-end multi-task generative adversarial network (MTGAN). In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi task network, which describes each super-resolution image patch with a real/fake score, object category scores, and bounding box regression off sets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training. Extensive experiments on the challenging COCO dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolution image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.