mtybilly/Instruct2DS huggingface dataset:
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Authors: Ruizhi Deng, Bo Li, Fisher Yu, Mingyan Liu, Dawn Song Chaowei Xiao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can mislead DNNs to make arbitrary incorrect predictions. While adversarial examples are mainly studied in classification, special properties may be inherited for those targeting on segmentation models which require additional components such as dilated convolutions and multiscale processing. In this paper, we aim to characterize adversarial examples based on spatial context information in segmentation. We observe that spatial consistency information can be potentially leveraged to recognize/detect adversarial examples robustly even when facing adaptive attacker who has access to the model and detection strategies. We also show that adversarial examples based on attacks we considered barely transfer among models, even though transferability is common in classification. Our observations shed new light on developing adversarial attacks and defenses to better understand the vulnerabilities of DNNs.
Deep Bilinear Learning for RGB-D Action Recognition
Authors: Wei-Shi Zheng, Jiahui Pan, Jianhuang Lai, Jianguo Zhang Jian-Fang Hu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we focus on exploring modality-temporal mutual information for RGB-D action recognition. In order to learn time-varying information and multi-modal features jointly, we propose a novel deep bilinear learning framework. In the framework, we propose bilinear blocks that consist of two linear pooling layers for pooling the input cube features from both modality and temporal directions, separately. To capture rich modality-temporal information and facilitate our deep bilinear learning, a new action feature called modality-temporal cube is presented in a tensor structure for characterizing RGB-D actions from a comprehensive perspective. Our method is extensively tested on two public datasets with four different evaluation settings, and the results show that the proposed method outperforms the state-of-the-art approaches.
Coded Two-Bucket Cameras for Computer Vision
Authors: Navid Sarhangnejad, Zhengfan Xia, Nikita Gusev, Nikola Katic, Roman Genov, Kiriakos N. Kutulakos Mian Wei
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We introduce coded two-bucket (C2B) imaging, a new operating principle for computational sensors with applications in active 3D shape estimation and coded-exposure imaging. A C2B sensor modulates the light arriving at each pixel by controlling which of the pixel's two "buckets" should integrate it. C2B sensors output two images per video frame---one per bucket---and allow rapid, fully-programmable, per-pixel control of the active bucket. Using these properties as a starting point, we (1) develop an image formation model for these sensors, (2) couple them with programmable light sources to acquire illumination mosaics, i.e., images of a scene under many different illumination conditions whose pixels have been multiplexed onto the sensor plane and acquired in one shot, and (3) show how to process illumination mosaics to acquire time-varying depth or normal maps of dynamic scenes at the sensor's native resolution. We present the first experimental demonstration of these capabilities, using a fully functional C2B camera prototype. Key to this prototype is a C2B sensor that was designed by us, fabricated in a standard CMOS imaging technology, and demonstrated for the first time in this paper.
Few-Shot Human Motion Prediction via Meta-Learning
Authors: Yu-Xiong Wang, Deva Ramanan, Jose M. F. Moura Liang-Yan Gui
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Human motion prediction, forecasting human motion in a few milliseconds conditioning on a historical 3D skeleton sequence, is a long-standing problem in computer vision and robotic vision. Existing forecasting algorithms rely on extensive annotated motion capture data and are brittle to novel actions. This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning. More precisely, our approach is based on the insight that having a good generalization from few examples relies on both a generic initial model and an effective strategy for adapting this model to novel tasks. To accomplish this, we propose proactive and adaptive meta-learning (PAML) that introduces a novel combination of model-agnostic meta-learning and model regression networks and unifies them into an integrated, end-to-end framework. By doing so, our meta-learner produces a generic model initialization through aggregating contextual information from a variety of prediction tasks, while this model can effectively adapt to a specific task by leveraging learning-to-learn knowledge about how to transform few-shot model parameters to many-shot model parameters. The resulting PAML predictor model significantly improves the prediction performance on the heavily benchmarked H3.6M dataset in the small-sample size regime.
Recycle-GAN: Unsupervised Video Retargeting
Authors: Shugao Ma, Deva Ramanan, Yaser Sheikh Aayush Bansal
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Authors: Ping Luo, Jianping Shi, Xiaoou Tang Xingang Pan
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN’s modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning. IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances. This work has three key contributions. (1) By delving into IN and BN, we disclose that IN learns features that are invariant to appearance changes, such as colors, styles, and virtuality/reality, while BN is essential for preserving content related information. (2) IBN-Net can be applied to many advanced deep architectures, such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their performance without increasing computational cost. (3) When applying the trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves comparable improvements as domain adaptation methods, even without using data from the target domain. With IBN-Net, we won the 1st place on the WAD 2018 Challenge Drivable Area track, with an mIoU of 86.18%.
Learning Shape Priors for Single-View 3D Completion and Reconstruction
Authors: Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T. Freeman, Joshua B. Tenenbaum Jiajun Wu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: there are usually multiple plausible shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given the input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both types of ambiguities aforementioned. Experiments demonstrate that ShapeHD outperforms state-of-the-arts by a large margin on both shape completion and shape reconstruction.
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
Authors: Haozhi Huang, Lin Ma, Wei Liu, Tong Zhang, Yugang Jiang Minjun Li
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Recent studies on unsupervised image-to-image translation have made remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss. However, such unsupervised methods may generate inferior results when the image resolution is high or the two image domains are of significant appearance differences, such as the translations between semantic layouts and natural images in the Cityscapes dataset. In this paper, we propose novel Stacked Cycle-Consistent Adversarial Networks (SCANs) by decomposing a single translation into multi-stage transformations, which not only boost the image translation quality but also enable higher resolution image-toimage translation in a coarse-to-fine fashion. Moreover, to properly exploit the information from the previous stage, an adaptive fusion block is devised to learn a dynamic integration of the current stage’s output and the previous stage’s output. Experiments on multiple datasets demonstrate that our proposed approach can improve the translation quality compared with previous single-stage unsupervised methods.
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Authors: Quoc-Huy Tran, M. Zeeshan Zia, Paul Vernaza, Manmohan Chandraker Mohammed E. Fathy
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors that outperform hand-crafted descriptors on many problems. We demonstrate that commonly used metric learning approaches do not optimally leverage the feature hierarchies learned in a Convolutional Neural Network (CNN), especially when applied to the task of geometric feature matching. While a metric loss applied to the deepest layer of a CNN, is often expected to yield ideal features irrespective of the task, in fact the growing receptive field as well as striding effects cause shallower features to be better at high precision matching tasks. We leverage this insight together with explicit supervision at multiple levels of the feature hierarchy for better regularization, to learn more effective descriptors in the context of geometric matching tasks. Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks. We propose concrete CNN architectures employing these ideas, and evaluate them on multiple datasets for 2D and 3D geometric matching as well as optical flow, demonstrating state-of-the-art results and generalization across datasets.
A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines
Authors: Tiago Dias, Srikumar Ramalingam Pedro Miraldo
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a minimal solution for pose estimation using both points and lines for a multi-perspective camera. In this paper, we treat the multi-perspective camera as a collection of rigidly attached perspective cameras. These type of imaging devices are useful for several computer vision applications that require a large coverage such as surveillance, self-driving cars, and motion-capture studios. While prior methods have considered the cases using solely points or lines, the hybrid case involving both points and lines has not been solved for multi-perspective cameras. We present the solutions for two cases. In the first case, we are given 2D to 3D correspondences for two points and one line. In the later case, we are given 2D to 3D correspondences for one point and two lines. We show that the solution for the case of two points and one line can be formulated as a fourth degree equation. This is interesting because we can get a closed-form solution and thereby achieve high computational efficiency. The later case involving two lines and one point can be mapped to an eighth degree equation. We show simulations and real experiments to demonstrate the advantages and benefits over existing methods.
Key-Word-Aware Network for Referring Expression Image Segmentation
Authors: Hongliang Li, Fanman Meng, Qingbo Wu Hengcan Shi
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Referring expression image segmentation aims to segment out the object referred by a natural language query expression. Without considering the specific properties of visual and textual information, existing works usually deal with this task by directly feeding a foreground/background classifier with cascaded image and text features, which are extracted from each image region and the whole query, respectively. On the one hand, they ignore that each word in a query expression makes different contributions to identify the desired object, which requires a differential treatment in extracting text feature. On the other hand, the relationships of different image regions are not considered as well, even though they are greatly important to eliminate the undesired foreground object in accordance with specific query. To address aforementioned issues, in this paper, we propose a key-word-aware network, which contains a query attention model and a key-word-aware visual context model. In extracting text features, the query attention model attends to assign higher weights for the words which are more important for identifying object. Meanwhile, the key-word-aware visual context model describes the relationships among different image regions, according to corresponding query. Our proposed method outperforms state-of-the-art methods on two referring expression image segmentation databases.
Dynamic Conditional Networks for Few-Shot Learning
Authors: Jian Zhao, Shuicheng Yan, Jiashi Feng Fang Zhao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i.e, only a few training samples are available for each condition. DCCN consists of dual subnets: DyConvNet contains a dynamic convolutional layer with a bank of basis filters; CondiNet predicts a set of adaptive weights from conditional inputs to linearly combine the basis filters. In this manner, a specific convolutional kernel can be dynamically obtained for each conditional input. The filter bank is shared between all conditions thus only a low-dimension weight vector needs to be learned. This significantly facilitates the parameter learning across different conditions when training data are limited. We evaluate DCCN on four tasks which can be formulated as conditional model learning, including specific object counting, multi-modal image classification, phrase grounding and identity based face generation. Extensive experiments demonstrate the superiority of the proposed model in the conditional few-shot learning setting.
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks
Authors: Fredo Durand Miika Aittala
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image. Our novel convolutional architecture has a simultaneous view of all frames in the burst, and by construction treats them in an order-independent manner. This enables it to effectively detect and leverage subtle cues scattered across different frames, while ensuring that each frame gets a full and equal consideration regardless of its position in the sequence. We train the network with richly varied synthetic data consisting of camera shake, realistic noise, and other common imaging defects. The method demonstrates consistent state of the art burst image restoration performance for highly degraded sequences of real-world images, and extracts accurate detail that is not discernible from any of the individual frames in isolation.
Learning Type-Aware Embeddings for Fashion Compatibility
Authors: Bryan A. Plummer, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar, David Forsyth Mariya I. Vasileva
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3-5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries.
Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching
Authors: Sudipta N. Sinha, Marc Pollefeys Johannes L. Schonberger
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Semi-Global Matching (SGM) uses an aggregation scheme to combine costs from multiple 1D scanline optimizations that tends to hurt its accuracy in difficult scenarios. We propose replacing this aggregation scheme with a new learning-based method that fuses disparity proposals estimated using scanline optimization. Our proposed SGM-Forest algorithm solves this problem using per-pixel classification. SGM-Forest currently ranks 1st on the ETH3D stereo benchmark and is ranked competitively on the Middlebury 2014 and KITTI 2015 benchmarks. It consistently outperforms SGM in challenging settings and under difficult training protocols that demonstrate robust generalization, while adding only a small computational overhead to SGM.
Dividing and Aggregating Network for Multi-view Action Recognition
Authors: Wanli Ouyang, Wen Li, Dong Xu Dongang Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we propose a new Dividing and Aggregating Network (DA-Net) for multi-view action recognition. In our DA-Net, we learn view-independent representations shared by all views at lower layers, while we learn one view-specific representation for each view at higher layers. We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers. The view classifier is used to predict how likely each video belongs to each view. Finally, the predicted view probabilities from multiple views are used as the weights when fusing the prediction scores of view-specific action classifiers. We also propose a new approach based on the conditional random field (CRF) formulation to pass message among view-specific representations from different branches to help each other. Comprehensive experiments on two benchmark datasets clearly demonstrate the effectiveness of our proposed DA-Net for multi-view action recognition.
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Authors: Naoto Yokoya, Jian Xu, Xiaoxiang Zhu Danfeng Hong
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called joint and progressive learning strategy (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
Image Inpainting for Irregular Holes Using Partial Convolutions
Authors: Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro Guilin Liu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose to use partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other inpainting methods to validate our approach.
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps
Authors: Tobias Weyand, Jack Sim, Bohyung Han Paul Hongsuck Seo
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information. This task is inherently challenging since many photos have only few, possibly ambiguous cues to their geolocation. Recent work has cast this task as a classification problem by partitioning the earth into a set of discrete cells that correspond to geographic regions. The granularity of this partitioning presents a critical trade-off; using fewer but larger cells results in lower location accuracy while using more but smaller cells reduces the number of training examples per class and increases model size, making the model prone to overfitting. To tackle this issue, we propose a simple but effective algorithm, combinatorial partitioning, which generates a large number of fine-grained output classes by intersecting multiple coarse-grained partitionings of the earth. Each classifier votes for the fine-grained classes that overlap with their respective coarse-grained ones. This technique allows us to predict locations at a fine scale while maintaining sufficient training examples per class. Our algorithm achieves the state-of-the-art performance in location recognition on multiple benchmark datasets.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Authors: Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam Liang-Chieh Chen
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at url{https://github.com/tensorflow/models/tree/master/research/deeplab}.
Large Scale Urban Scene Modeling from MVS Meshes
Authors: Shuhan Shen, Xiang Gao, Zhanyi Hu Lingjie Zhu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper we present an effcient modeling framework for large scale urban scenes. Taking surface meshes derived from multi- view-stereo systems as input, our algorithm outputs simplied models with semantics at different levels of detail (LODs). Our key observation is that urban building is usually composed of planar roof tops connected with vertical walls. There are two major steps in our framework: segmentation and building modeling. The scene is first segmented into four classes with a Markov random field combining height and image features. In the following modeling step, various 2D line segments sketching the roof boundaries are detected and slice the plane into faces. Through assigning each face with a roof plane, the final model is constructed by extruding the faces to the corresponding planes. By combining geometric and appearance cues together, the proposed method is robust and fast compared to the state-of-the-art algorithms.
Generalized Loss-Sensitive Adversarial Learning with Manifold Margins
Authors: Guo-Jun Qi Marzieh Edraki
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: The classic Generative Adversarial Net and its variants can be roughly categorized into two large families: the unregularized ver- sus regularized GANs. By relaxing the non-parametric assumption on the discriminator in the classic GAN, the regularized GANs have better generalization ability to produce new samples drawn from the real dis- tribution. It is well known that the real data like natural images are not uniformly distributed over the whole data space. Instead, they are often restricted to a low-dimensional manifold of the ambient space. Such a manifold assumption suggests the distance over the manifold should be a better measure to characterize the distinct between real and fake sam- ples. Thus, we define a pullback operator to map samples back to their data manifold, and a manifold margin is defined as the distance between the pullback representations to distinguish between real and fake sam- ples and learn the optimal generators. We justify the effectiveness of the proposed model both theoretically and empirically.
Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World
Authors: Fabio Lanzi, Simone Calderara, Andrea Palazzi, Roberto Vezzani, Rita Cucchiara Matteo Fabbri
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Multi-People Tracking in an open-world setting requires a special effort in precise detection. Moreover, temporal continuity in the detection phase gains more importance when scene cluttering introduces the challenging problems of occluded targets. For the purpose, we propose a deep network architecture that jointly extracts people body parts and associates them across short temporal spans. Our model explicitly deals with occluded body parts, by hallucinating plausible solutions of not visible joints. We propose a new end-to-end architecture composed by four branches (visible heatmaps, occluded heatmaps, part affinity fields and temporal affinity fields) fed by a time linker feature extractor. To overcome the lack of surveillance data with tracking, body part and occlusion annotations we created the vastest Computer Graphics dataset for people tracking in urban scenarios by exploiting a photorealistic videogame. It is up to now the vastest dataset (about 500.000 frames, almost 10 million body poses) of human body parts for people tracking in urban scenarios. Our architecture trained on virtual data exhibits good generalization capabilities also on public real tracking benchmarks, when image resolution and sharpness are high enough, producing reliable tracklets useful for further batch data association or re-id modules.
W-TALC: Weakly-supervised Temporal Activity Localization and Classification
Authors: Sourya Roy, Amit K. Roy-Chowdhury Sujoy Paul
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement. Learning from weak labels may be a potential solution towards reducing such manual labeling effort. Recent years have witnessed a substantial influx of tagged videos on the Internet, which can serve as a rich source of weakly-supervised training data. Specifically, the correlations between videos with similar tags can be utilized to temporally localize the activities. Towards this goal, we present W-TALC, a Weakly-supervised Temporal Activity Localization and Classification framework using only video-level labels. The proposed network can be divided into two sub-networks, namely the Two-Stream based feature extractor network and a weakly-supervised module, which we learn by optimizing two complimentary loss functions. Qualitative and quantitative results on two challenging datasets - Thumos14 and ActivityNet1.2, demonstrate that the proposed method is able to detect activities at a fine granularity and achieve better performance than current state-of-the-art methods.
Viewpoint Estimation---Insights & Model
Authors: Ayellet Tal Gilad Divon
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: This paper addresses the problem of viewpoint estimation of an object in a given image. It presents five key insights and a CNN that is based on them. The network's major properties are as follows. (i) The architecture jointly solves detection, classification, and viewpoint estimation. (ii) New types of data are added and trained on. (iii) A novel loss function, which takes into account both the geometry of the problem and the new types of data, is propose. Our network allows a substantial boost in performance: from 36.1% gained by SOTA algorithms to 45.9%.
Relaxation-Free Deep Hashing via Policy Gradient
Authors: Liangliang Ren, Jiwen Lu, Jie Zhou Xin Yuan
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we propose a simple yet effective relaxation-free method to learn more effective binary codes via policy gradient for scalable image search. While a variety of deep hashing methods have been proposed in recent years, most of them are confronted by the dilemma to obtain optimal binary codes in a truly end-to-end manner with non-smooth sign activations. Unlike existing methods which usually employ a general relaxation framework to adapt to the gradient-based algorithms, our approach formulates the non-smooth part of the hashing network as sampling with a stochastic policy, so that the retrieval performance degradation caused by the relaxation can be avoided. Specifically, our method directly generates the binary codes and maximizes the expectation of rewards for similarity preservation, where the network can be trained directly via policy gradient. Hence, the differentiation challenge for discrete optimization can be naturally addressed, which leads to effective gradients and binary codes. Extensive experimental results on three benchmark datasets validate the effectiveness of the proposed method.
Rolling Shutter Pose and Ego-motion Estimation using Shape-from-Template
Authors: Omar Ait-Aider, Adrien Bartoli Yizhen Lao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a new method for the absolute camera pose problem (PnP) which handles Rolling Shutter (RS) effects. Unlike all existing methods which perform 3D-2D registration after augmenting the Global Shutter (GS) projection model with the velocity parameters under various kinematic models, we propose to use local differential constraints. These are established by drawing an analogy with Shape-from-Template (SfT). The main idea consists in considering that RS distortions due to camera ego-motion during image acquisition can be interpreted as virtual deformations of a template captured by a GS camera. Once the virtual deformations have been recovered using SfT, the camera pose and ego-motion are computed by registering the deformed scene on the original template. This 3D-3D registration involves a 3D cost function based on the Euclidean point distance, more physically meaningful than the re-projection error or the algebraic distance based cost functions used in previous work. Results on both synthetic and real data show that the proposed method outperforms existing RS pose estimation techniques in terms of accuracy and stability of performance in various configurations.
Learning to Capture Light Fields through a Coded Aperture Camera
Authors: Yuto Kobayashi, Keita Takahashi, Toshiaki Fujii, Hajime Nagahara Yasutaka Inagaki
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a learning-based framework for acquiring a light field through a coded aperture camera. Acquiring a light field is a challenging task due to the amount of data. To make the acquisition process efficient, coded aperture cameras were successfully adopted; using these cameras, a light field is computationally reconstructed from several images that are acquired with different aperture patterns. However, it is still difficult to reconstruct a high-quality light field from only a few acquired images. To tackle this limitation, we formulated the entire pipeline of light field acquisition from the perspective of an auto-encoder. This auto-encoder was implemented as a stack of fully convolutional layers and was trained end-to-end by using a collection of training samples. We experimentally show that our method can successfully learn good image-acquisition and reconstruction strategies. With our method, light fields consisting of 5 x 5 or 8 x 8 images can be successfully reconstructed only from a few acquired images. Moreover, our method achieved superior performance over several state-of-the-art methods. We also applied our method to a real prototype camera to show that it is capable of capturing a real 3-D scene.
Variable Ring Light Imaging: Capturing Transient Subsurface Scattering with An Ordinary Camera
Authors: Art Subpa-asa, Yuta Asano, Mihoko Shimano, Imari Sato Ko Nishino
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Subsurface scattering plays a significant role in determining the appearance of real-world surfaces. A light ray penetrating into the subsurface is repeatedly scattered and absorbed by particles along its path before reemerging from the outer interface, which determines its spectral radiance. We introduce a novel imaging method that enables the decomposition of the appearance of a fronto-parallel real-world surface into images of light with bounded path lengths, i.e., transient subsurface light transport. Our key idea is to observe each surface point under a variable ring light: a circular illumination pattern of increasingly larger radius centered on it. We show that the path length of light captured in each of these observations is naturally lower-bounded by the ring light radius. By taking the difference of ring light images of incrementally larger radii, we compute transient images that encode light with bounded path lengths. Experimental results on synthetic and complex real-world surfaces demonstrate that the recovered transient images reveal the subsurface structure of general translucent inhomogeneous surfaces. We further show that their differences reveal the surface colors at different surface depths. The proposed method is the first to enable the unveiling of dense and continuous subsurface structures from steady-state external appearance using ordinary camera and illumination.
Deep Video Generation, Prediction and Completion of Human Action Sequences
Authors: Chunyan Bai, Yu-Wing Tai, Chi-Keung Tang Haoye Cai
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Current video generation/prediction/completion results are limited, due to the severe ill-posedness inherent in these three problems. In this paper, we focus on human action videos, and propose a general, two-stage deep framework to generate human action videos with no constraints or arbitrary number of constraints, which uniformly address the three problems: video generation given no input frames, video prediction given the first few frames, and video completion given the first and last frames. To solve video generation from scratch, we build a two-stage framework where we first train a deep generative model that generates human pose sequences from random noise, and then train a skeleton-to-image network to synthesize human action videos given the human pose sequences generated. To solve video prediction and completion, we exploit our trained model and conduct optimization over the latent space to generate videos that best suit the given input frame constraints. With our novel method, we sidestep the original ill-posed problems and produce for the first time high-quality video generation/prediction/completion results of much longer duration. We present quantitative and qualitative evaluations to show that our approach outperforms state-of-the-art methods in all three tasks.
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
Authors: Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, Kevin Murphy George Papandreou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-part associations using part-based modeling. Our model employs a convolutional network which learns to detect individual keypoints and predict their relative displacements, allowing us to group keypoints into person pose instances. Further, we propose a part-induced geometric embedding descriptor which allows us to associate semantic person pixels with their corresponding person instance, delivering instance-level person segmentations. Our system is based on a fully-convolutional architecture and allows for efficient inference, with runtime essentially independent of the number of people present in the scene. Trained on COCO data alone, our system achieves COCO test-dev keypoint average precision of 0.665 using single-scale inference and 0.687 using multi-scale inference, significantly outperforming all previous bottom-up pose estimation systems. We are also the first bottom-up method to report competitive results for the person class in the COCO instance segmentation task, achieving a person category average precision of 0.417.
Robust image stitching with multiple registrations
Authors: Chen Wang, Richard Strong Bowen, Emil Keyder, Michael Krainin, Ce Liu, Ramin Zabih Charles Herrmann
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Panorama creation is one of the most widely deployed techniques in computer vision. In addition to industry applications such as Google Street View, it is also used by millions of consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, which picks a single transformation of each source image to align it to the other inputs, seam finding, which selects a source image for each pixel in the final result, and blending, which fixes minor visual artifacts. Here, we observe that the use of a single registration often leads to errors, especially in scenes with significant depth variation or object motion. We propose instead the use of multiple registrations, permitting regions of the image at different depths to be captured with greater accuracy. MRF inference techniques naturally extend to seam finding over multiple registrations, and we show here that their energy functions can be readily modified with new terms that discourage duplication and tearing, common problems that are exacerbated by the use of multiple registrations. Our techniques are closely related to layer-based stereo, and move image stitching closer to explicit scene modeling. Experimental evidence demonstrates that our techniques often generate significantly better panoramas when there is substantial motion or parallax.
Learning to Solve Nonlinear Least Squares for Monocular Stereo
Authors: Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison Ronald Clark
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Sum-of-squares objective functions are very popular in computer vision algorithms. However, these objective functions are not always easy to optimize. The underlying assumptions made by solvers are often not satisfied and many problems are inherently ill-posed. In this paper, we propose a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities. Unlike traditional approaches, the proposed solver requires no hand-crafted regularizers or priors as these are implicitly learned from the data. We apply our method to the problem of motion stereo ie. jointly estimating the motion and scene geometry from pairs of images of a monocular sequence. We show that our learned optimizer is able to efficiently and effectively solve this challenging optimization problem.
Direct Sparse Odometry With Rolling Shutter
Authors: Nikolaus Demmel, Vladyslav Usenko, Jorg Stuckler, Daniel Cremers David Schubert
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method that incorporates a rolling-shutter model. Our approach extends direct sparse odometry which performs direct bundle adjustment of a set of recent keyframe poses and the depths of a sparse set of image points. We estimate the velocity at each keyframe and impose a constant-velocity prior for the optimization. In this way, we obtain a near real-time, accurate direct VO method. Our approach achieves improved results on challenging rolling-shutter sequences over state-of-the-art global-shutter VO.
A Zero-Shot Framework for Sketch based Image Retrieval
Authors: Shiva Krishna Reddy, Ashish Mishra, Anurag Mittal Sasi Kiran Yelamarthi
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Sketch-based image retrieval (SBIR) is the task of retrieving images from a natural image database that correspond to a given hand-drawn sketch. Ideally, an SBIR model should learn to associate components in the sketch (say, feet, tail, etc.) with the corresponding components in the image. However, current evaluation methods simply focus only on coarse-grained evaluation where the focus is on retrieving images which belong to the same class as the sketch but not necessarily having the same components as in the sketch. As a result, existing methods simply learn to associate sketches with classes seen during training and hence fail to generalize to unseen classes. In this paper, we propose a new bench mark for zero-shot SBIR where the model is evaluated on novel classes that are not seen during training. We show through extensive experiments that existing models for SBIR which are trained in a discriminative setting learn only class specific mappings and fail to generalize to the proposed zero-shot setting. To circumvent this, we propose a generative approach for the SBIR task by proposing deep conditional generative models which take the sketch as an input and fill the missing information stochastically. Experiments on this new benchmark created from the "Sketchy" dataset, which is a large-scale database of sketch-photo pairs demonstrate that the performance of these generative models is significantly better than several state-of-the-art approaches in the proposed zero-shot framework of the coarse-grained SBIR task.