pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Geometry-Aware Human Motion Prediction
Authors: Liang-Yan Gui, Yu-Xiong Wang, Xiaodan Liang, Jose M. F.  Moura
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Human motion prediction, motion prediction, Prediction

We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework. Current approaches suffer from the problem of prediction discontinuities and may fail to predict human-like motion in longer time horizons due to error accumulation. We address these critical issues by incorporating local geometric structure constraints and regularizing predictions with plausible temporal smoothness and continuity from a global perspective. Specifically, rather than using the conventional Euclidean loss, we propose a novel frame-wise geodesic loss as a geometrically meaningful, more precise distance measurement. Moreover, inspired by the adversarial training mechanism, we present a new learning procedure to simultaneously validate the sequence-level plausibility of the prediction and its coherence with the input sequence by introducing two global recurrent discriminators. An unconditional, fidelity discriminator and a conditional, continuity discriminator are jointly trained along with the predictor in an adversarial manner. Our resulting adversarial geometry-aware encoder-decoder (AGED) model significantly outperforms state-of-the-art deep learning based approaches on the heavily benchmarked H3.6M dataset in both short-term and long-term predictions.
WildDash - Creating Hazard-Aware Benchmarks
Authors: Oliver Zendel, Katrin Honauer, Markus Murschitz, Daniel Steininger, Gustavo Fernandez Dominguez
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Semantic Segmentation

Test datasets should contain many different challenging aspects so that the robustness and real-world applicability of algorithms can be assessed. In this work, we present a new test dataset for semantic and instance segmentation for the automotive domain. We have conducted a thorough risk analysis to identify situations and aspects that can reduce the output performance for these tasks. Based on this analysis we have designed our new dataset. Meta-information is supplied to mark which individual visual hazards are present in each test case. Furthermore, a new benchmark evaluation method is presented that uses the meta-information to calculate the robustness of a given algorithm with respect to the individual hazards. We show how this new approach allows for a more expressive characterization of algorithm robustness by comparing three baseline algorithms.
RefocusGAN: Scene Refocusing using a Single Image
Authors: Parikshit Sakurikar, Ishit Mehta, Vineeth N. Balasubramanian, P. J. Narayanan
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deblurring, Position

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblur-then-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter, we are able to achieve generic free-form refocusing over a single image.
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
Authors: Wei Liu, Shengcai Liao, Weidong Hu, Xuezhi Liang, Xiao Chen
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pedestrian Detection

Though Faster R-CNN based two-stage detectors have witnessed significant boost in pedestrian detection accuracy, it is still slow for practical applications. One solution is to simplify this working flow as a single-stage detector. However, current single-stage detectors (e.g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks. This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN. Specifically, a structurally simple but effective module called emph{Asymptotic Localization Fitting} (ALF) is proposed, which stacks a series of predictors to directly evolve the default anchor boxes of SSD step by step into improving detection results. As a result, during training the latter predictors enjoy more and better-quality positive samples, meanwhile harder negatives could be mined with increasing IoU thresholds. On top of this, an efficient single-stage pedestrian detection architecture (denoted as ALFNet) is designed, achieving state-of-the-art performance on CityPersons and Caltech, two of the largest pedestrian detection benchmarks, and hence resulting in an attractive pedestrian detector in both accuracy and speed. Code is available at href{https://github.com/VideoObjectSearch/ALFNet}{https://github.com/VideoObjectSearch/ALFNet}.
Generative Adversarial Network with Spatial Attention for Face Attribute Editing
Authors: Gang Zhang, Meina Kan, Shiguang Shan, Xilin Chen
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Data Augmentation, Face Recognition, Generative Adversarial Network

Face attribute editing aims at editing the face image with the given attribute. Most existing works employ Generative Adversarial Network (GAN) to operate face attribute editing. However, these methods  inevitably change the attribute-irrelevant regions, as shown in Fig.~ef{fig1}. Therefore, we introduce the spatial attention mechanism into GAN framework (referred to as SaGAN), to only alter the attribute-specific region and keep the rest unchanged. Our approach SaGAN consists of a generator and a discriminator. The generator contains an attribute manipulation network (AMN) to edit the face image, and a spatial attention network (SAN) to localize the attribute-specific region which restricts the alternation of AMN within this region. The discriminator endeavors to distinguish the generated images from the real ones, and classify the face attribute. Experiments demonstrate that our approach can achieve promising visual results, and keep those attribute-irrelevant regions unchanged. Besides, our approach can benefit the face recognition by data augmentation.
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset
Authors: Jamie Ray, Heng Wang, Du Tran, YuFei Wang, Matt Feiszli, Lorenzo Torresani, Manohar Paluri
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Temporal Action Localization, Transfer Learning

This paper introduces a large-scale, multi-label and multitask video dataset named Scenes-Objects-Actions (SOA). Most prior video datasets are based on a predened taxonomy, which is used to de- ne the keyword queries issued to search engines. The videos retrieved by the search engines are then veried for correctness by human annotators. Datasets collected in this manner tend to generate high classication accuracy as search engines typically rank easy" videos rst. The SOA dataset adopts a dierent approach. We rely on uniform sampling to get a better representations of videos on the Web. Trained annotators are asked to provide free-form text labels describing each video in three dierent aspects: scene, object and action. These raw labels are then merged, split and renamed to generate a taxonomy for SOA. All the annotations are veried again based on the taxonomy. The nal dataset includes 562K videos with 3.64M annotations spanning 49 categories for scenes, 356 for objects, and 148 for actions. We show that datasets collected in this way are quite challenging by evaluating existing popular video models on SOA. We provide in-depth analysis about the performance of dierent models on SOA, and highlight potential new directions in video classication. A key-feature of SOA is that it enables the empirical study of correlation among scene, object and action recognition in video. We present results of this study and further analyze the potential of using the information learned from one task to improve the others. We compare SOA with existing datasets in the context of transfer learning and demonstrate that pre-training on SOA consistently improves the accuracy on a wide variety of datasets. We believe that the challenges presented by SOA oer the opportunity for further advancement in video analysis as we progress from single-label classication towards a more comprehensive understanding of video data.
Descending, lifting or smoothing: Secrets of robust cost optimization
Authors: Christopher Zach, Guillaume Bourmaud
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9

Robust cost optimization is the challenging task of fitting a large number   of parameters to data points containing a significant and unknown fraction of   outliers.  In this work we identify three classes of deterministic   second-order algorithms that are able to tackle this type of optimization   problem: direct approaches that aim to optimize the robust cost   directly with a second order method, lifting-based approaches that add so   called lifting variables to embed the given robust cost function into a   higher dimensional space, and graduated optimization methods that solve a   sequence of smoothed cost functions. We study each of these classes of   algorithms and propose improvements either to reduce their computational   time or to make them find better local minima. Finally, we experimentally   demonstrate the superiority of our improved graduated optimization method   over the state of the art algorithms both on synthetic and real data for   four different problems.
Understanding Perceptual and Conceptual Fluency at a Large Scale
Authors: Shengli Hu, Ali Borji
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Causal Inference

We create a dataset of 543,758 logo designs spanning 39 industrial categories and 216 countries. We experiment and compare how different deep convolutional neural network (hereafter, DCNN) architectures, pretraining protocols,  and weight initializations perform in predicting design memorability and likability. We propose and provide estimation methods based on training DCNNs to extract and evaluate two independent constructs for designs: perceptual distinctiveness (``perceptual fluency'' metrics) and ambiguity in meaning (``conceptual fluency'' metrics) of each logo. We provide evidences of causal inference that both constructs significantly affect memory for a logo design, consistent with cognitive elaboration theory. The effect on liking, however, is interactive, consistent with processing fluency (e.g., Lee and Labroo (2004), Landwehr et al. (2011).
Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation
Authors: Daniel Maurer, Nico Marniok, Bastian Goldluecke, Andres Bruhn
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Optical Flow Estimation

Many recent energy-based methods for optical flow estimation rely on a good initialization that is typically provided by some kind of feature matching. So far, however, these initial matching approaches are rather general: They do not incorporate any additional information that could help to improve the accuracy or the robustness of the estimation. In particular, they do not exploit potential cues on the camera poses and the thereby induced rigid motion of the scene. In the present paper, we tackle this problem. To this end, we propose a novel structure-from-motion-aware PatchMatch approach that, in contrast to existing matching techniques, combines two hierarchical feature matching methods: a recent two-frame PatchMatch approach for optical flow estimation (general motion) and a specifically tailored three-frame PatchMatch approach for rigid scene reconstruction (SfM). While the motion PatchMatch serves as baseline with good accuracy, the SfM counterpart takes over at occlusions and other regions with insufficient information. Experiments with our novel SfM-aware PatchMatch approach demonstrate its usefulness. They not only show excellent results for all major benchmarks (KITTI 2012/2015, MPI Sintel), but also improvements up to 50 percent compared to a PatchMatch approach without structure information.
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions
Authors: Joel Janai, Fatma Guney, Anurag Ranjan, Michael Black, Andreas Geiger
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Occlusion Handling, Optical Flow Estimation

Learning optical flow with neural networks is hampered by the need for obtaining training data with associated ground truth. Unsupervised learning is a promising direction, yet the performance of current unsupervised methods is still limited. In particular, the lack of proper occlusion handling in commonly used data terms constitutes a major source of error. While most optical flow methods process pairs of consecutive frames, more advanced occlusion reasoning can be realized when considering multiple frames. In this paper, we propose a framework for unsupervised learning of optical flow and occlusions over multiple frames. More specifically, we exploit the minimal configuration of three frames to strengthen the photometric loss and explicitly reason about occlusions. We demonstrate that our multi-frame, occlusion-sensitive formulation outperforms existing unsupervised two-frame methods and even produces results on par with some fully supervised methods.
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
Authors: Keisuke Tateno, Nassir Navab, Federico Tombari
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Semantic Segmentation, Style Transfer

There is a high demand of 3D data for 360Â° panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e.g., omnidirectional cameras) as well as visualizing in 3D (e.g., head mounted displays) panoramic images and videos. At the same time, 3D sensors able to capture 3D panoramic data are expensive and/or hardly available. To fill this gap, we propose a learning approach for panoramic depth map estimation from a single image. Thanks to a specifically developed distortion-aware deformable convolution filter, our method can be trained by means of conventional perspective images, then used to regress depth for panoramic images, thus bypassing the effort needed to create annotated panoramic training dataset. We also demonstrate our approach for emerging tasks such as panoramic monocular SLAM, panoramic semantic segmentation and panoramic style transfer.
Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation
Authors: Shaofei Wang, Alexander Ihler, Konrad Kording, Julian Yarkony
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Multi-Person Pose Estimation, Pose Estimation

We present a novel approach to solve dynamic programs (DP), which are frequent in computer vision, on tree-structured graphs with exponential node state space.  Typical DP approaches have to enumerate the joint state space of two adjacent nodes on every edge of the tree to compute the optimal messages. Here we propose an algorithm based on Nested Benders Decomposition (NBD) which iteratively lower-bounds the message on every edge and promises to be far more efficient. We apply our NBD algorithm along with a novel Minimum Weight Set Packing (MWSP) formulation to a multi-person pose estimation problem. While our algorithm is provably optimal at termination it operates in linear time for practical DP problems, gaining up to 500x speed up over traditional DP algorithm which have polynomial complexity.
Joint optimization for compressive video sensing and reconstruction under hardware constraints
Authors: Michitaka Yoshida, Akihiko Torii, Masatoshi Okutomi, Kenta Endo, Yukinobu Sugiyama, Rin-ichiro Taniguchi, Hajime Nagahara
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Compressive Sensing

Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and reconstructing the sub-frames from the single compressed frame. It is known that spatially and temporally random exposures provide the most balanced compression in terms of signal recovery. However, sensors that achieve a fully random exposure on each pixel cannot be easily realized in practice because the circuit of the sensor becomes complicated and incompatible with the sensitivity and resolution.Therefore, it is necessary to design an exposure pattern by considering the constraints enforced by hardware. In this paper, we propose a method of jointly optimizing the exposure patterns of compressive sensing and the reconstruction framework under hardware constraints. By conducting a simulation and actual experiments, we demonstrated that the proposed framework can reconstruct multiple sub-frame images with higher quality.
Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification
Authors: Eric Muller-Budack, Kader Pustu-Iren, Ralph Ewerth
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Image Classification, Image Retrieval, Photo geolocation estimation, Retrieval, Scene Classification

While the successful estimation of a photo's geolocation enables a number of interesting applications, it is also a very challenging task. Due to the complexity of the problem, most existing approaches are restricted to specific areas, imagery, or worldwide landmarks. Only a few proposals predict GPS coordinates without any limitations. In this paper, we introduce several deep learning methods, which pursue the latter approach and treat geolocalization as a classification problem where the earth is subdivided into geographical cells. We propose to exploit hierarchical knowledge of multiple partitionings and additionally extract and take the photo's scene content into account, i.e., indoor, natural, or urban setting etc. As a result, contextual information at different spatial resolutions as well as more specific features for various environmental settings are incorporated in the learning process of the convolutional neural network. Experimental results on two benchmarks demonstrate the effectiveness of our approach outperforming the state of the art while using a significant lower number of training images and without relying on retrieval methods that require an appropriate reference dataset.
Universal Sketch Perceptual Grouping
Authors: Ke Li, Kaiyue Pang, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Honggang Zhang
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Object, Retrieval, Sketch-Based Image Retrieval

In this work we aim to develop a universal sketch grouper.  That is, a grouper  that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts.  The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute  the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20,000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses.  The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR).
License Plate Detection and Recognition in Unconstrained Scenarios
Authors: Sergio Montazzolli Silva, Claudio Rosito Jung
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: License Plate Detection, License Plate Recognition, Optical Character Recognition, Optical Character Recognition (OCR)

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.
Affine Correspondences between Central Cameras for Rapid Relative Pose Estimation
Authors: Ivan Eichhardt, Dmitry Chetverikov
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pose Estimation, Translation

This paper presents a novel algorithm to estimate the relative pose, i.e. the 3D rotation and translation of two cameras, from two affine correspondences (ACs) considering any central camera model. The solver is built on new epipolar constraints describing the relationship of an AC and any central views. We also show that the pinhole case is a specialization of the proposed approach. Benefiting from the low number of required correspondences, robust estimators like LO-RANSAC need fewer samples, and thus terminate earlier than using the five-point method. Tests on publicly available datasets containing pinhole, fisheye and catadioptric camera images confirmed that the method often leads to results superior to the state-of-the-art in terms of geometric accuracy.
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations
Authors: Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: model

We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations. Our goal is to answer a simple question: why did a model classify an image as being of class A instead of class B? Existing approaches to model interpretation, including saliency and explanation-by-nearest neighbor, fail to visually illustrate examples of transformations required for a specific input to alter a model's prediction. On the other hand, algorithms for creating decision-boundary crossing transformations (e.g., adversarial examples) produce differences that are visually imperceptible and do not enable insightful explanation. To address this we introduce ExplainGAN, a generative model that produces visually perceptible decision-boundary crossing transformations. These transformations provide high-level conceptual insights which illustrate how a model makes decisions. We validate our model using both traditional quantitative interpretation metrics and introduce a new validation scheme for our approach and generative models more generally.