mtybilly/Instruct2DS huggingface dataset:
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
Authors: Xiaofeng Liu, B.V.K Vijaya Kumar, Chao Yang, Qingming Tang, Jane You
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or based on their independent quality assessment. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) at the feature level. Specifically, we propose a dependency-aware attention control (DAC) network, which resorts to actor-critic reinforcement learning for sequential attention decision of each image embedding to fully exploit the rich correlation cues among the unordered images. Moreover, its sample-efficient variant with off-policy experience replay is introduced to speed up the learning process. The pose-guided representation scheme can further boost the performance at the extremes of the pose variation. We show that our method leads to the state-of-the-art accuracy on IJB-A dataset and also generalizes well in several video-based face recognition tasks, extit{e.g.}, YTF and Celebrity-1000.
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN
Authors: Yunlong Wang, Fei Liu, Zilei Wang, Guangqi Hou, Zhenan Sun, Tieniu Tan
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. In this paper, an end-to-end deep learning framework is proposed to solve these problems by exploring Pseudo 4DCNN. Specifically, 2D strided convolutions operated on stacked EPIs and detail-restoration 3D CNNs connected with angular conversion are assembled to build the Pseudo 4DCNN. The key advantage is to efficiently synthesize dense 4D light fields from a sparse set of input views. The learning framework is well formulated as an entirely trainable problem, and all the weights can be recursively updated with standard backpropagation. The proposed framework is compared with state-of-the-art approaches on both genuine and synthetic light field databases, which achieves significant improvements of both image quality (+2dB higher) and computational efficiency (over 10X faster). Furthermore, the proposed framework shows good performances in real-world applications such as biometrics and depth estimation.
Iterative Crowd Counting
Authors: Viresh Ranjan, Hieu Le, Minh Hoai
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resolution density maps, where the first branch generates a low resolution density map and the second branch incorporates the prediction and feature maps from the first branch to generate a high resolution density map. We also propose a multi-stage extension of our approach where each stage in the pipeline utilizes the predictions from all the previous stages. Empirical comparison with the previous state-of-the-art crowd counting methods shows that our method achieves the lowest mean absolute error on three challenging crowd counting benchmarks: Shanghaitech, WorldExpo'10, and UCF datasets.
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
Authors: Weixuan Chen, Daniel McDuff
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism.
On the Solvability of Viewing Graphs
Authors: Matthew Trager, Brian Osserman, Jean Ponce
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: A set of fundamental matrices relating pairs of cameras in some configuration can be represented as edges of a ``viewing graph''. Whether or not these fundamental matrices are generically sufficient to recover the global camera configuration depends on the structure of this graph. We study characterizations of ``solvable'' viewing graphs, and present several new results that can be applied to determine which pairs of views may be used to recover all camera parameters. We also discuss strategies for verifying the solvability of a graph computationally.
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Authors: Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, Yanzhi Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning. By using ADMM, the original nonconvex optimization problem is decomposed into two subproblems that are solved iteratively. One of these subproblems can be solved using stochastic gradient descent, the other can be solved analytically. Besides, our method achieves a fast convergence rate. The weight pruning results are very promising and consistently outperform the prior work. On the LeNet-5 model for the MNIST data set, we achieve 71.2 times weight reduction without accuracy loss. On the AlexNet model for the ImageNet data set, we achieve 21 times weight reduction without accuracy loss. When we focus on the convolutional layer pruning for computation reductions, we can reduce the total computation by five times compared with the prior work (achieving a total of 13.4 times weight reduction in convolutional layers). Our models and codes are released at https://github.com/KaiqiZhang/admm-pruning.
Multimodal Dual Attention Memory for Video Story Question Answering
Authors: Kyung-Min Kim, Seong-Ho Choi, Jin-Hwa Kim, Byoung-Tak Zhang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these latent concepts. Multimodal fusion is performed after the dual attention processes (late fusion). Using this processing pipeline, MDAM learns to infer a high-level vision-language joint representation from an abstraction of the full video content. We evaluate MDAM on PororoQA and MovieQA datasets which have large-scale QA annotations on cartoon videos and movies, respectively. For both datasets, MDAM achieves new state-of-the-art results with significant margins compared to the runner-up models. We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies. We also perform qualitative analysis by visualizing the inference mechanisms of MDAM.
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
Authors: Yonghyun Kim, Bong-Nam Kang, Daijin Kim
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Most of the recent successful methods in accurate object detection build on the convolutional neural networks (CNN). However, due to the lack of scale normalization in CNN-based detection methods, the activated channels in the feature space can be completely different according to a scale and this difference makes it hard for the classifier to learn samples. We propose a Scale Aware Network (SAN) that maps the convolutional features from the different scales onto a scale-invariant subspace to make CNN-based detection methods more robust to the scale variation, and also construct a unique learning method which considers purely the relationship between channels without the spatial information for the efficient learning of SAN. To show the validity of our method, we visualize how convolutional features change according to the scale through a channel activation matrix and experimentally show that SAN reduces the feature differences in the scale space. We evaluate our method on VOC PASCAL and MS COCO dataset. We demonstrate SAN by conducting several experiments on structures and parameters. The proposed SAN can be generally applied to many CNN-based detection methods to enhance the detection accuracy with a slight increase in the computing time.
Single Shot Scene Text Retrieval
Authors: Lluis Gomez, Andres Mafla, Marcal Rusinol, Dimosthenis Karatzas
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image database. Our experiments demonstrate that the proposed architecture outperforms previous state-of-the-art while it offers a significant increase in processing speed.
Dynamic Task Prioritization for Multitask Learning
Authors: Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, Li Fei-Fei
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose dynamic task prioritization for multitask learning. This allows a model to dynamically prioritize difficult tasks during training, where difficulty is inversely proportional to performance, and where difficulty changes over time. In contrast to curriculum learning, where easy tasks are prioritized above difficult tasks, we present several studies showing the importance of prioritizing difficult tasks first. We observe that imbalances in task difficulty can lead to unnecessary emphasis on easier tasks, thus neglecting and slowing progress on difficult tasks. Motivated by this finding, we introduce a notion of dynamic task prioritization to automatically prioritize more difficult tasks by adaptively adjusting the mixing weight of each task's loss objective. Additional ablation studies show the impact of the task hierarchy, or the task ordering, when explicitly encoded in the network architecture. Our method outperforms existing multitask methods and demonstrates competitive results with modern single-task models on the COCO and MPII datasets.
Self-supervised Knowledge Distillation Using Singular Value Decomposition
Authors: Seung Hyun Lee, Dae Ha Kim, Byung Cheol Song
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN has limited range of use, and the knowledge of T-DNN is insufficiently transferred to S-DNN. To improve the quality of the transferred knowledge from T-DNN, we propose a new knowledge distillation using singular value decomposition (SVD). In addition, we define a knowledge transfer as a self-supervised task and suggest a way to continuously receive information from T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of the T-DNN can be up to 1.1% better than the T-DNN in terms of classification accuracy. Also assuming the same computational cost, our S-DNN outperforms the S-DNN driven by the state-of-the-art distillation with a performance advantage of 1.79%. code is available on https://github.com/sseung0703/SSKD_SVD.
Transductive Centroid Projection for Semi-supervised Large-scale Recognition
Authors: Yu Liu, Guanglu Song, Jing Shao, Xiao Jin, Xiaogang Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Conventional deep semi-supervised learning methods, such as recursive clustering and training process, suffer from cumulative error and high computational complexity when collaborating with Convolutional Neural Networks. To this end, we design a simple but effective learning mechanism that merely substitutes the last fully-connected layer with the proposed Transductive Centroid Projection (TCP) module. It is inspired by the observation of the weights in classification layer (called extit{anchors}) converge to the central direction of each class in hyperspace. Specifically, we design the TCP module by dynamically adding an extit{ad hoc anchor} for each cluster in one mini-batch. It essentially reduces the probability of the inter-class conflict and enables the unlabelled data functioning as labelled data. We inspect its effectiveness with elaborate ablation study on seven public face/person classification benchmarks. Without any bells and whistles, TCP can achieve significant performance gains over most state-of-the-art methods in both fully-supervised and semi-supervised manners.
Deep Shape Matching
Authors: Filip Radenovic, Giorgos Tolias, Ondrej Chum
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
Authors: Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Authors: Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy. The traditional modular pipeline heavily relies on hand-designed rules and the pre-processing perception system while the supervised learning-based models are limited by the accessibility of extensive human experience. We present a general and principled Controllable Imitative Reinforcement Learning (CIRL) approach which successfully makes the driving agent achieve higher success rates based on only vision inputs in a high-fidelity car simulator. To alleviate the low exploration efficiency for large continuous action space that often prohibits the use of classical RL on challenging real tasks, our CIRL explores over a reasonably constrained action space guided by encoded experiences that imitate human demonstrations, building upon Deep Deterministic Policy Gradient (DDPG). Moreover, we propose to specialize adaptive policies and steering-angle reward designs for different control signals (i.e. follow, straight, turn right, turn left) based on the shared representations to improve the model capability in tackling with diverse cases. Extensive experiments on CARLA driving benchmark demonstrate that CIRL substantially outperforms all previous methods in terms of the percentage of successfully completed episodes on a variety of goal-directed driving tasks. We also show its superior generalization capability in unseen environments. To our knowledge, this is the first successful case of the learned driving policy by reinforcement learning in the high-fidelity simulator, which performs better than supervised imitation learning.
EC-Net: an Edge-aware Point set Consolidation Network
Authors: Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based {em edge-aware} technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is {em edge-aware/}. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.
Part-Activated Deep Reinforcement Learning for Action Prediction
Authors: Lei Chen, Jiwen Lu, Zhanjie Song, Jie Zhou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structural information of human body diminishes the capacity of features to describe actions. To address this, we design a PA-DRL to exploit the structure of the human body by extracting skeleton proposals under a deep reinforcement learning framework. Specifically, we extract features from different parts of the human body individually and activate the action-related parts in features to enhance the representation. Our method not only exploits the structure information of the human body, but also considers the saliency part for expressing actions. We evaluate our method on three popular action prediction datasets: UT-Interaction, BIT-Interaction and UCF101. Our experimental results demonstrate that our method achieves the performance with state-of-the-arts.
Learning to Navigate for Fine-grained Classification
Authors: Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of fine-grained bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.
Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model
Authors: Jie Guo, Zuojian Zhou, Limin Wang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a sparse and low-rank reflection model for specular highlight detection and removal using a single input image. This model is motivated by the observation that the specular highlight of a natural image usually has large intensity but is rather sparsely distributed while the remaining diffuse reflection can be well approximated by a linear combination of several distinct colors with a sparse and low-rank weighting matrix. We further impose the non-negativity constraint on the weighting matrix as well as the highlight component to ensure that the model is purely additive. With this reflection model, we reformulate the task of highlight removal as a constrained nuclear norm and $l_1$-norm minimization problem which can be solved effectively by the augmented Lagrange multiplier method. Experimental results show that our method performs well on both synthetic images and many real-world examples and is competitive with previous methods, especially in some challenging scenarios featuring natural illumination, hue-saturation ambiguity and strong noises.
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Authors: Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically un- successful when the domains require larger shape change. Inspired by semantic segmentation, we introduce a discriminator with dilated convo- lutions which is able to use information from across the entire image to train a more context-aware generator. This is coupled with a multi-scale perceptual loss which is better able to represent error in the underly- ing shape of objects. We demonstrate that this design is more capable of representing shape deformation in a challenging toy dataset, plus in complex mappings with significant dataset variation between humans, dolls, and anime faces, and between cats and dogs.
Scalable Exemplar-based Subspace Clustering on Class-Imbalanced Data
Authors: Chong You, Chi Li, Daniel P. Robinson, Rene Vidal
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Subspace clustering methods based on expressing each data point as a linear combination of a few other data points (e.g., sparse subspace clustering) have become a popular tool for unsupervised learning due to their empirical success and theoretical guarantees. However, their performance can be affected by imbalanced data distributions and large-scale datasets. This paper presents an exemplar-based subspace clustering method to tackle the problem of imbalanced and large-scale datasets. The proposed method searches for a subset of the data that best represents all data points as measured by the $ell_1$-norm of the representation coefficients. To solve our model efficiently, we introduce a farthest first search algorithm which iteratively selects the least well-represented point as an exemplar. When data comes from a union of subspaces, we prove that the computed subset contains enough exemplars from each subspace for expressing all data points even if the data are imbalanced. Our experiments demonstrate that the proposed method outperforms state-of-the-art subspace clustering methods in two large-scale image datasets that are imbalanced. We also demonstrate the effectiveness of our method on unsupervised data subset selection for a face image classification task.
3D Ego-Pose Estimation via Imitation Learning
Authors: Ye Yuan, Kris Kitani
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Ego-pose estimation, i.e., estimating a person's 3D pose with a single wearable camera, has many potential applications in activity monitoring. For these applications, both accurate and physically plausible estimates are desired, with the latter often overlooked by existing work. Traditional computer vision-based approaches using temporal smoothing only take into account the kinematics of the motion without considering the physics that underlies the dynamics of motion, which leads to pose estimates that are physically invalid. Motivated by this, we propose a novel control-based approach to model human motion with physics simulation and use imitation learning to learn a video-conditioned control policy for ego-pose estimation. Our imitation learning framework allows us to perform domain adaption to transfer our policy trained on simulation data to real-world data. Our experiments with real egocentric videos show that our method can estimate both accurate and physically plausible 3D ego-pose sequences without observing the cameras wearer's body.
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
Authors: Satwik Kottur, Jose M. F. Moura, Devi Parikh, Dhruv Batra, Marcus Rohrbach
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Visual dialog entails answering a series of questions grounded in an image, using dialog history as context. In addition to the challenges found in visual question answering (VQA), which can be seen as one-round dialog, visual dialog encompasses several more. We focus on one such problem called ‘visual coreference resolution’ that involves determining which words, typically noun phrases and pronouns, ‘co-refer’ to the same entity/object instance in an image. This is crucial, especially for pronouns (e.g., ‘it'), as the dialog agent must first link it to a previous coreference (e.g., ‘boat'), and only then can rely on the visual grounding of the coreference ‘boat' to reason about the pronoun `it'. Prior work (in visual dialog) models visual coreference resolution either (a) implicitly via a memory network over history, or (b) at a coarse level for the entire question; and not explicitly at a phrase level of granularity. In this work, we propose a neural module network architecture for visual dialog by introducing two novel modules---Refer and Exclude---that perform explicit, grounded, coreference resolution at a finer word level. We demonstrate the effectiveness of our model on MNIST Dialog, a visually simple yet coreference-wise complex dataset, by achieving near perfect accuracy, and on VisDial, a large and challenging visual dialog dataset on real images, where our model outperforms other approaches, and is more interpretable, grounded, and consistent qualitatively.
LSQ++: Lower running time and higher recall in multi-codebook quantization
Authors: Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos, James J. Little
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases. Work in MCQ is heavily focused on lowering quantization error, thereby improving distance estimation and recall on benchmarks of visual descriptors at a fixed memory budget. However, recent studies and methods in this area are hard to compare against each other, because they use different datasets, different protocols, and, perhaps most importantly, different computational budgets. In this work, we first benchmark a series of MCQ baselines on an equal footing and provide an analysis of their recall-vs-runtime performance. We observe that local search quantization (LSQ) is in practice much faster than its competitors, but is not the most accurate method in all cases. We then introduce two novel improvements that render LSQ (i) more accurate and (ii) faster. These improvements are easy to implement, and define a new state of the art in MCQ.
A Hybrid Model for Identity Obfuscation by Face Replacement
Authors: Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition, becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.
Depth-aware CNN for RGB-D Segmentation
Authors: Weiyue Wang, Ulrich Neumann
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Authors: Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
Authors: Yinda Zhang, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Michael Schoenberg, Shahram Izadi, Thomas Funkhouser, Sean Fanello
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of 1/30th of a pixel; it does not suffer from the common over-smoothing issues of previous approaches; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.
Weakly- and Semi-Supervised Panoptic Segmentation
Authors: Qizhu Li, Anurag Arnab, Philip H.S. Torr
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both ``thing'' and ``stuff'' classes, and thus explain all the pixels in the image. ``Thing'' classes are weakly-supervised with bounding boxes, and ``stuff'' with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.
Selfie Video Stabilization
Authors: Jiyang Yu, Ravi Ramamoorthi
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background motion can be analyzed using optical flow. We use second derivative of temporal trajectory of selected pixels as the measure of smoothness. Our algorithm stabilizes selfie videos by minimizing the smoothness measure of the back-ground, regularized by the motion of the foreground. Experiments show that our method outperforms state-of-the-art general video stabilization techniques in selfie videos.
Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network
Authors: Jinseok Park, Donghyeon Cho, Wonhyuk Ahn, Heung-Kyu Lee
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Double JPEG detection is essential for detecting various image manipulations. This paper proposes a novel deep convolutional neural network for double JPEG detection using statistical histogram features from each block with a vectorized quantization table. In contrast to previous methods, the proposed approach handles mixed JPEG quality factors and is suitable for real-world situations. We collected real-world JPEG images from the image forensic service and generated a new double JPEG dataset with 1120 quantization tables to train the network. The proposed approach was verified experimentally to produce a state-of-the-art performance, successfully detecting various image manipulations.
Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering
Authors: Tianshu Yu, Junchi Yan, Wei Liu, Baoxin Li
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Multi-graph matching refers to finding correspondences across graphs, which are traditionally solved by matching all the graphs in a single batch. However in real-world applications, graphs are often collected incrementally, rather than once for all. In this paper, we present an incremental multi-graph matching approach, which deals with the arriving graph utilizing the previous matching results under the global consistency constraint. When a new graph arrives, rather than re-optimizing over all graphs, we propose to partition graphs into subsets with certain topological structure and conduct optimization within each subset. The partitioning procedure is guided by the diversity within partitions and randomness over iterations, and we present an interpretation showing why these two factors are essential. The final matching results are calculated over all subsets via an intersection graph. Extensive experimental results on synthetic and real image datasets show that our algorithm notably improves the efficiency without sacrificing the accuracy.
DeepTAM: Deep Tracking and Mapping
Authors: Huizhong Zhou, Benjamin Ummenhofer, Thomas Brox
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6~DOF tracking competes with RGB-D tracking algorithms.We compare favorably against strong classic and deep learning powered dense depth algorithms.
R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
Authors: Nicholas Rhinehart, Kris M. Kitani, Paul Vernaza
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a method to forecast a vehicle's ego-motion as a distribution over spatiotemporal paths, conditioned on features (e.g., from LIDAR and images) embedded in an overhead map. The method learns a policy inducing a distribution over simulated trajectories that is both diverse (produces most paths likely under the data) and precise (mostly produces paths likely under the data). This balance is achieved through minimization of a symmetrized cross-entropy between the distribution and demonstration data. By viewing the simulated-outcome distribution as the pushforward of a simple distribution under a simulation operator, we obtain expressions for the cross-entropy metrics that can be efficiently evaluated and differentiated, enabling stochastic-gradient optimization. We propose concrete policy architectures for this model, discuss our evaluation metrics relative to previously-used metrics, and demonstrate the superiority of our method relative to state-of-the-art methods in both the KITTI dataset and a similar but novel and larger real-world dataset explicitly designed for the vehicle forecasting domain.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Authors: Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, Yu Qiao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features from point clouds. SpiderCNN is comprised of units called SpiderConv, which extend convolutional operations from regular grids to irregular point sets that can be embedded in R^n, by parametrizing a family of convolutional filters. We design the filter as a product of a simple step function that captures local geodesic information and a Taylor polynomial that ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical architecture from classical CNNs, which allows it to extract semantic deep features. Experiments on ModelNet40 demonstrate that SpiderCNN achieves state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Authors: Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled rawly from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling massive amount of noisy labels and data imbalance effectively. We design a new learning curriculum by measuring the complexity of data using its distribution density in a feature space, and rank the complexity in an unsupervised manner. This allows for an efficient implementation of curriculum learning on large-scale web images, resulting in a high-performance CNN model, where the negative impact of noisy labels is reduced substantially. Importantly, we show by experiments that those images with highly noisy labels can surprisingly improve the generalization capability of model, by serving as a manner of regularization. Our approaches obtain the state-of-the-art performance on four benchmarks, including Webvision, ImageNet, Clothing-1M and Food-101. With an ensemble of multiple models, we achieve a top-5 error rate of 5.2% on the Webvision challenge cite{li2017webvision} for 1000-category classification, which is the top performance that surpasses other results by a large margin of about 50% relative error rate. Codes and models are available at: https://github.com/guoshengcv/CurriculumNet.
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
Authors: Eddy Ilg, Tonmoy Saikia, Margret Keuper, Thomas Brox
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Occlusions play an important role in optical flow and disparity estimation, since matching costs are not available in occluded areas and occlusions indicate motion boundaries. Moreover, occlusions are relevant for motion segmentation and scene flow estimation. In this paper, we present an efficient learning-based approach to estimate occlusion areas jointly with optical flow or disparities. The estimated occlusions and motion boundaries clearly improve over the state of the art. Moreover, we present networks with state-of-the-art performance on the popular KITTI benchmark and good generic performance. Making use of the estimated occlusions, we also show imprved results on motion segmentation and scene flow estimation.