pwc-archive/papers-with-abstracts huggingface dataset:
Synthetically Supervised Feature Learning for Scene Text Recognition
Authors: Yang Liu, Zhaowen Wang, Hailin Jin, Ian Wassell 
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Scene Text Recognition, Synthetic Data Generation

We address the problem of image feature learning for scene text recognition. The image features in the state-of-the-art methods are learned from large-scale synthetic image datasets. However, most methods only rely on outputs of the synthetic data generation process, namely realistically looking images, and completely ignore the rest of the process. We propose to leverage the parameters that lead to the output images to improve image feature learning. Specifically, for every image out of the data generation process, we obtain the associated parameters and render another "clean" image that is free of select distortion factors that are applied to the output image. Because of the absence of distortion factors, the clean image tends to be easier to recognize than the original image. We design a multi-task network with an encoder-discriminator-generator architecture to guide the feature of the original image toward that of the clean image. The experiments show that our method significantly outperforms the state-of-the-art methods on standard scene text recognition benchmarks. Furthermore, we show that without explicitly handling, our method works on challenging cases where input images contain severe geometric distortion, such as text on a curved path.
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360Â° Panoramic Imagery
Authors: Greire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Object Detection, Autonomous Vehicles, Depth Estimation, Monocular Depth Estimation, object-detection, Object Detection

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360Â° panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360Â° panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.
HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration
Authors: B. Eckart, K. Kim, J. Kautz
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Navigation, GPU, Point Cloud Registration, Scene Recognition

Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality. In this paper, we present a new registration algorithm that is able to achieve state-of-the-art speed and accuracy through its use of a Hierarchical Gaussian Mixture representation. Our method, Hierarchical Gaussian Mixture Registration (HGMR), constructs a top-down multi-scale representation of point cloud data by recursively running many small-scale data likelihood segmentations in parallel on a GPU. We leverage the resulting representation using a novel optimization criterion that adaptively finds the best scale to perform data association between spatial subsets of point cloud data. Compared to previous Iterative Closest Point and GMM-based techniques, our tree-based point association algorithm performs data association in logarithmic-time while dynamically adjusting the level of detail to best match the complexity and spatial distribution characteristics of local scene geometry. In addition, unlike other GMM methods that restrict covariances to be isotropic, our new PCA-based optimization criterion well-approximates the true MLE solution even when fully anisotropic Gaussian covariances are used. Efficient data association, multi-scale adaptability, and a robust MLE approximation produce an algorithm that is up to an order of magnitude both faster and more accurate than current state-of-the-art on a wide variety of 3D datasets captured from LiDAR to structured light.
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking
Authors: Chenglong Li, Chengli Zhu, Yan Huang, Jin Tang, Liang Wang
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Tracking, Rgb-T Tracking, Sparse Learning

Due to the complementary benefits of visible (RGB) and thermal infrared (T) data, RGB-T object tracking attracts more and more attention recently for boosting the performance under adverse illumination conditions. Existing RGB-T tracking methods usually localize a target object with a bounding box, in which the trackers or detectors is often affected by the inclusion of background clutter. To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking. Our approach relies on a novel cross-modal manifold ranking algorithm. First, we integrate the soft cross-modality consistency into the ranking model which allows the sparse inconsistency to account for the different properties between these two modalities. Second, we propose an optimal query learning method to handle label noises of queries. In particular, we introduce an intermediate variable to represent the optimal labels, and formulate it as a $l_1$-optimization based sparse learning problem. Moreover, we propose a single unified optimization algorithm to solve the proposed model with stable and efficient convergence behavior. Finally, the ranking results are incorporated into the patch-based object features to address the background effects, and the structured SVM is then adopted to perform RGB-T tracking. Extensive experiments suggest that the proposed approach performs well against the state-of-the-art methods on large-scale benchmark datasets.
Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation
Authors: Tao Song, Leiyu Sun, Di Xie, Haiming Sun, ShiLiang Pu
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pedestrian Detection

A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and temporal feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Ran- dom Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects signicantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.
Sub-GAN: An Unsupervised Generative Model via Subspaces
Authors: Jie Liang, Jufeng Yang, Hsin-Ying Lee, Kai Wang, Ming-Hsuan Yang
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generative Adversarial Network

The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data. However, it is difficult to fully exploit the distribution of complex data, like images and videos, due to the high dimensionality of ambient space. Sequentially, how to effectively guide the training of generative models is a crucial issue. In this paper, we present a subspace-based generative adversarial network (Sub-GAN) which simultaneously disentangles multiple latent subspaces and generates diverse samples correspondingly. Since the high-dimensional natural data usually lies on a union of low-dimensional subspaces which contain semantically extensive structure, Sub-GAN incorporates a novel clusterer that can interact with the generator and discriminator via subspace information. Unlike the traditional generative models, the proposed Sub-GAN can control the diversity of the generated samples via the multiplicity of the learned subspaces. Moreover, the Sub-GAN follows an unsupervised fashion to explore not only the visual classes but the latent continuous attributes. We demonstrate that our model can discover meaningful visual attributes which is hard to be annotated via strong supervision, e.g., the writing style of digits, thus avoid the mode collapse problem. Extensive experimental results show the competitive performance of the proposed method for both generating diverse images with satisfied quality and discovering discriminative latent subspaces.
Interactive Boundary Prediction for Object Selection
Authors: Hoang Le, Long Mai, Brian Price, Scott Cohen, Hailin Jin, Feng Liu
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Image Segmentation, Interactive Segmentation, Object, Prediction, Segmentation, Semantic Segmentation

Interactive image segmentation is critical for many image editing tasks. While recent advanced methods on interactive segmentation focus on the region-based paradigm, more traditional boundary-based methods such as Intelligent Scissor are still popular in practice as they allow users to have active control of the object boundaries. Existing methods for boundary-based segmentation solely rely on low-level image features, such as edges for boundary extraction, which limits their ability to adapt to high-level image content and user intention. In this paper, we introduce an interaction-aware method for boundary-based image segmentation. Instead of relying on pre-defined low-level image features, our method adaptively predicts object boundaries according to image content and user interactions. Therein, we develop a fully convolutional encoder-decoder network that takes both the image and user interactions (e.g. clicks on boundary points) as input and predicts semantically meaningful boundaries that match user intentions. Our method explicitly models the dependency of boundary extraction results on image content and user interactions. Experiments on two public interactive segmentation benchmarks show that our method significantly improves the boundary quality of segmentation results compared to state-of-the-art methods while requiring fewer user interactions.
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
Authors: Hongmei Song, Wenguan Wang, Sanyuan Zhao, Jianbing Shen, Kin-Man Lam
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: GPU, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Salient Object Detection, Video Semantic Segmentation

This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is first designed for simultaneously extracting spatial features at multiple scales. These spatial features are then concatenated and fed into an extended Deeper Bidirectional ConvLSTM (DB-ConvLSTM) to learn spatiotemporal information. Forward and backward ConvLSTM units are placed in two layers and connected in a cascaded way, encouraging information flow between the bi-directional streams and leading to deeper feature extraction. We further augment DB-ConvLSTM with a PDC-like structure, by adopting several dilated DB-ConvLSTMs to extract multi-scale spatiotemporal information.  Extensive experimental results show that our method outperforms previous video saliency models in a large margin, with a real-time speed of 20 fps on a single GPU. With unsupervised video object segmentation as an example application, the proposed model (with a CRF-based post-process) achieves state-of-the-art results on two popular benchmarks, well demonstrating its superior performance and high applicability.
Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders
Authors: Panna Felsen, Patrick Lucey, Sujoy Ganguly
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9

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).
X-ray Computed Tomography Through Scatter
Authors: Adam Geva, Yoav Y. Schechner, Yonatan Chernyak, Rajiv Gupta
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9

In current Xray CT scanners, tomographic reconstruction relies only on directly transmitted photons. The models used for reconstruction have regarded photons scattered by the body as noise or disturbance to be disposed of, either by acquisition hardware (an anti-scatter grid) or by the reconstruction software. This increases the radiation dose delivered to the patient. Treating these scattered photons as a source of information, we solve an inverse problem based on a 3D radiative transfer model that includes both elastic (Rayleigh) and inelastic (Compton) scattering. We further present ways to make the solution numerically efficient. The resulting tomographic reconstruction is more accurate than traditional CT, while enabling significant dose reduction and chemical decomposition. Demonstrations include both simulations based on a standard medical phantom and a real scattering tomography experiment.
Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency
Authors: Vincent Leroy, Jean-Sebastien Franco, Edmond Boyer
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Shape Reconstruction

The rise of virtual and augmented reality fuels an increased need for content suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper  the problem of 3D shape reconstruction from multi-view RGB images.  We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different  orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state-of-the-art-reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data.
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization
Authors: Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Xiaoning Qian, Yung-Yu Chuang
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Co-Salient Object Detection, Saliency Detection

In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN). Our method does not require any additional training data in the form of object masks. We decompose co-saliency detection into two sub-tasks, single-image saliency detection and cross-image co-occurrence region discovery corresponding to two novel unsupervised losses, the single-image saliency (SIS) loss and the co-occurrence (COOC) loss. The two losses are modeled on a graphical model where the former and the latter act as the unary and pairwise terms, respectively. These two tasks can be jointly optimized for generating co-saliency maps of high quality. Furthermore, the quality of the generated co-saliency maps can be enhanced via two extensions: map sharpening by self-paced learning and boundary preserving by fully connected conditional random fields. Experiments show that our method achieves superior results, even outperforming many supervised methods.
Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal
Authors: Jie Yang, Dong Gong, Lingqiao Liu, Qinfeng Shi
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Reflection Removal

Reflections often obstruct the desired scene when taking photos through glass panels. Removing unwanted reflection automatically from the photos is highly desirable. Traditional methods often impose certain priors or assumptions to target particular type(s) of reflection such as shifted double reflection, thus have difficulty to generalise to other types. Very recently a deep learning approach has been proposed. It learns a deep neural network that directly maps a reflection contaminated image to a background (target) image (ie reflection free image) in an end to end fashion, and outperforms the previous methods. We argue that, to remove reflection truly well, we should estimate the reflection and utilise it to estimate the background image. We propose a cascade deep neural network, which estimates both the background image and the reflection. This significantly improves reflection removal. In the cascade deep network, we use the estimated background image to estimate the reflection, and then use the estimated reflection to estimate the background image, facilitating our idea of seeing deeply and bidirectionally.
Learning Data Terms for Non-blind Deblurring
Authors: Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, Ming-Hsuan Yang
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deblurring

Existing deblurring methods mainly focus on developing effective image priors and assume that blurred images contain insignificant amounts of noise. However, state-of-the-art deblurring methods do not perform well on real-world images degraded with significant noise or outliers. To address these issues, we show that it is critical to learn data fitting terms beyond the commonly used L1 or L2 norm. We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers. Instead of learning the distribution of the data fitting errors, we directly learn the associated shrinkage function for the data term using a cascaded architecture, which is more flexible and efficient. Our analysis shows that the shrinkage functions learned at the intermediate stages can effectively suppress noise and preserve image structures. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation
Authors: Xuecheng Nie, Jiashi Feng, Shuicheng Yan
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human Parsing, Multi-Task Learning, Pose Estimation, Semantic Segmentation

This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation. It effectively  exploits mutual benefits from both tasks and simultaneously boosts their performance. Different from existing post-processing or multi-task learning based methods, MuLA predicts dynamic task-specific model parameters via recurrently leveraging  guidance information from its parallel tasks.  Thus MuLA can fast adapt parsing and pose models  to provide more powerful  representations  by incorporating information from their counterparts, giving more robust and accurate results. MuLA is implemented with convolutional neural networks and end-to-end trainable. Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part demonstrate the effectiveness of the proposed MuLA model with superior performance to well established baselines.
Statistically-motivated Second-order Pooling
Authors: Kaicheng Yu, Mathieu Salzmann 
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9

However, the nature of such operations is usually computationally expensive, and resulting vector representation orders of magnitude larger than first-order baselines. Here, by contrast, we introduce a statistically-motivated framework that projects the second-order descriptor into a compact vector while improving the representational power.  To this end, we design a parametric vectorization layer, which maps a covariance matrix, known to follow a Wishart distribution, to a vector whose elements can be shown to follow a Chi-square distribution. We then propose to make use of a square-root normalization, which makes the distribution of the resulting representation converge to a Gaussian, with which most classifiers of recent first-order networks complying. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.
Hierarchical Relational Networks for Group Activity Recognition and Retrieval
Authors: Mostafa S. Ibrahim, Greg Mori
Date: 2018-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Activity Recognition, Denoising, Group Activity Recognition, Retrieval

Modeling structured relationships between people in a scene is an important step toward visual understanding. We present a Hierarchical Relational Network that computes relational representations of people, given graph structures describing potential interactions. Each relational layer is fed individual person representations and a potential relationship graph.  Relational representations of each person are created based on their connections in this particular graph. We demonstrate the efficacy of this model by applying it in both supervised and unsupervised learning paradigms. First, given a video sequence of people doing a collective activity, the relational scene representation is utilized for multi-person activity recognition. Second, we propose a Relational Autoencoder model for unsupervised learning of features for action and scene retrieval. Finally, a Denoising Autoencoder variant is presented to infer missing people in the scene from their context. Empirical results demonstrate that this approach learns relational feature representations that can effectively discriminate person and group activity classes.