mtybilly/Instruct2DS huggingface dataset:
Joint optimization for compressive video sensing and reconstruction under hardware constraints
Authors: Akihiko Torii, Masatoshi Okutomi, Kenta Endo, Yukinobu Sugiyama, Rin-ichiro Taniguchi, Hajime Nagahara Michitaka Yoshida
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: 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.
Improved Structure from Motion Using Fiducial Marker Matching
Authors: Timothy Bretl, Derek Hoiem Joseph DeGol
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we present an incremental structure from motion (SfM) algorithm that signiﬁcantly outperforms existing algorithms when ﬁducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present. Our algorithm uses markers to limit potential incorrect image matches, change the order in which images are added to the reconstruction, and enforce new bundle adjustment constraints. To validate our algorithm, we introduce a new dataset with 16 image collections of large indoor scenes with challenging characteristics (e.g., blank hallways, glass facades, brick walls) and with markers placed throughout. We show that our algorithm produces complete, accurate reconstructions on all 16 image collections, most of which cause other algorithms to fail. Further, by selectively masking ﬁducial markers, we show that the presence of even a small number of markers can improve the results of our algorithm.
Deep Autoencoder for Combined Human Pose Estimation and Body Model Upscaling
Authors: Andrew Gilbert, Adrian Hilton, John Collomosse Matthew Trumble
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation for volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of 4x, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has potential for passive human behavior monitoring where there is a requirement for high fidelity estimation of human body shape and pose.
Integral Human Pose Regression
Authors: Bin Xiao, Fangyin Wei, Shuang Liang, Yichen Wei Xiao Sun
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as non-differentiable post-processing and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.
Convolutional Networks with Adaptive Inference Graphs
Authors: Serge Belongie Andreas Veit
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which layer to compute next. In this work, we propose convolutional networks with adaptive inference graphs (ConvNet-AIG) that adaptively define their network topology conditioned on the input image. Following a high-level structure similar to residual networks (ResNets), ConvNet-AIG decides for each input image on the fly which layers are needed. In experiments on ImageNet we show that ConvNet-AIG learns distinct inference graphs for different categories. Both ConvNet-AIG with 50 and 101 layers outperform their ResNet counterpart, while using 20% and 33% less computations respectively. By grouping parameters into layers for related classes and only executing relevant layers, ConvNet-AIG improves both efficiency and overall classification quality. Lastly, we also study the effect of adaptive inference graphs on the susceptibility towards adversarial examples. We observe that ConvNet-AIG shows a higher robustness than ResNets, complementing other known defense mechanisms.
A Dataset and Architecture for Visual Reasoning with a Working Memory
Authors: Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo Guangyu Robert Yang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.
Video Compression through Image Interpolation
Authors: Nayan Singhal, Philipp Krahenbuhl Chao-Yuan Wu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep learning codec. Our codec builds one simple idea: Video compression is repeated image interpolation. It thus benefits from recent advances in deep image interpolation and generation. Our deep video codec outperforms today's prevailing codecs, such as H.261, MPEG4 Part 2, and performs on par with H.264.
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Authors: Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, Mubarak Shah Haroon Idrees
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.
Affinity Derivation and Graph Merge for Instance Segmentation
Authors: Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, Yan Lu Yiding Liu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance. In our scheme, we use two neural networks with similar structure. One is to predict pixel level semantic score and the other is designed to derive pixel affinities. Regarding pixels as the vertexes and affinities as edges, we then propose a simple yet effective graph merge algorithm to cluster pixels into instances. Experimental results show that our scheme can generate fine grained instance mask. With Cityscapes training data, the proposed scheme achieves 27.3 AP on test set.
Progressive Structure from Motion
Authors: Michal Havlena, Luc Van Gool Alex Locher
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
Authors: Salih Karagoz, Emre Akbas Muhammed Kocabas
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. On the COCO keypoints dataset, our pose estimation method outperforms all previous bottom-up methods both in accuracy (+4-point mAP over previous best result) and speed; it also performs on par with the best top-down methods while being at least 4x faster. Our method is the fastest real time system with ~23 frames/sec.
Self-Calibrating Isometric Non-Rigid Structure-from-Motion
Authors: Adrien Bartoli, Daniel Pizarro Shaifali Parashar
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present self-calibrating isometric non-rigid structure- from-motion (SCIso-NRSfM), the first method to reconstruct a non-rigid object from at least three monocular images with constant but unknown focal length. The majority of NRSfM methods using the perspective cam- era simply assume that the calibration is known. SCIso-NRSfM leverages the recent powerful differential approaches to NRSfM, based on formu- lating local polynomial constraints, where local means correspondence- wise. In NRSfM, the local shape may be solved from these constraints. In SCIso-NRSfM, the difficulty is to also solve for the focal length as a global variable. We propose to eliminate the shape using resultants, obtaining univariate polynomials for the focal length only, whose sum of squares can then be globally minimized. SCIso-NRSfM thus solves for the focal length by integrating the constraints for all correspondences and the whole image set. Once this is done, the local shape is easily re- covered. Our experiments show that its performance is very close to the state-of-the-art methods that use a calibrated camera.
Using Object Information for Spotting Text
Authors: Adams Wai Kin Kong Shitala Prasad
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Text spotting, also called text detection, is a challenging computer vision task because of cluttered backgrounds, diverse imaging environments, various text sizes and similarity between some objects and characters, e.g., tyre and ’o’. However, text spotting is a vital step in numerous AI and computer vision systems, such as autonomous robots and systems for visually impaired. Due to its potential applications and commercial values, researchers have proposed various deep architectures and methods for text spotting. These methods and architectures concentrate only on text in images, but neglect other information related to text. There exists a strong relationship between certain objects and the presence of text, such as signboards or the absence of text, such as trees. In this paper, a text spotting algorithm based on text and object dependency is proposed. The proposed algorithm consists of two sub-convolutional neural networks and three training stages. For this study, a new NTU-UTOI dataset containing over 22k non-synthetic images with 277k bounding boxes for text and 42 text-related object classes is established. According to our best knowledge, it is the second largest non-synthetic text image database. Experimental results on three benchmark datasets with clutter backgrounds, COCO-Text, MSRA-TD500 and SVT show that the proposed algorithm provides comparable performance to state-of-the-art text spotting methods. Experiments are also performed on our newly established dataset to investigate the effectiveness of object information for text spotting. The experimental results indicate that the object information contributes significantly on the performance gain.
Modality Distillation with Multiple Stream Networks for Action Recognition
Authors: Pietro Morerio, Vittorio Murino Nuno C. Garcia
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) dataset could be accurately designed to include a variety of sensory inputs, it is often the case that not all modalities are available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to learn robust representations leveraging multimodal data in the training stage, while considering limitations at test time, such as noisy or missing modalities. This paper presents a new approach for multimodal video action recognition, developed within the unified frameworks of distillation and privileged information, named generalized distillation. Particularly, we consider the case of learning representations from depth and RGB videos, while relying on RGB data only at test time. We propose a new approach to train an hallucination network that learns to distill depth features through multiplicative connections of spatiotemporal representations, leveraging soft labels and hard labels, as well as distance between feature maps. We report state-of-the-art results on video action classification on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
Authors: Tong Qin, andShaojie Shen Peiliang Li
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D bounding box using end-to-end approaches, we propose to use the easy-to-labeled 2D detection and discrete viewpoint classification together with a light-weight semantic inference method to obtain rough 3D object measurements. Based on the object-aware-aided camera pose tracking which is robust in dynamic environments, in combination with our novel dynamic object bundle adjustment (BA) approach to fuse temporal sparse feature correspondences and the semantic 3D measurement model, we obtain 3D object pose, velocity and anchored dynamic point cloud estimation with instance accuracy and temporal consistency. The performance of our proposed method is demonstrated in diverse scenarios. Both the ego-motion estimation and object localization are compared with the state-of-of-the-art solutions.
AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos
Authors: Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang Zheng Shou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temporal boundary). This raises the interest of addressing TAL with weak supervision, namely only video-level annotations are available during training). However, the state-of-the-art weakly-supervised TAL methods only focus on generating good Class Activation Sequence (CAS) over time but conduct simple thresholding on CAS to localize actions. In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance. We propose a novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed segment-level supervision for training such a boundary predictor. Our method achieves dramatically improved performance: under the IoU threshold 0.5, our method improves mAP on THUMOS'14 from 13.7% to 21.2% and mAP on ActivityNet from 7.4% to 27.3%. It is also very encouraging to see that our weakly-supervised method achieves comparable results with some fully-supervised methods.
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
Authors: Arjun Karpur, Chuang Gan, Linjie Luo, Qixing Huang Xingyi Zhou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.
Visual-Inertial Object Detection and Mapping
Authors: Stefano Soatto Xiaohan Fei
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a method to populate an unknown environment with models of previously seen objects, placed in a Euclidean reference frame that is inferred causally and on-line using monocular video along with inertial sensors. The system we implement returns a sparse point cloud for the regions of the scene that are visible but not recognized as a previously seen object, and a detailed object model and its pose in the Euclidean frame otherwise. The system includes bottom-up and top-down components, whereby deep networks trained for detection provide likelihood scores for object hypotheses provided by a nonlinear filter, whose state serves as memory. Additional networks provide likelihood scores for edges, which complements detection networks trained to be invariant to small deformations. We test our algorithm on existing datasets, and also introduce the VISMA dataset, that provides ground truth pose, point-cloud map, and object models, along with time-stamped inertial measurements.
FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification
Authors: Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, Dacheng Tao Xiaoqing Yin
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Images captured by sheye lenses violate the pinhole camera assumption and suer from distortions. Rectication of sheye images is therefore a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions from single sheye images. In contrast to conventional approaches, which focus on extracting hand-crafted features from input images, our method learns high-level semantics and low-level appearance features simultaneously to estimate the distortion parameters. To facilitate training, we construct a synthesized dataset that covers various scenes and distortion parameter settings. Experiments on both synthesized and real-world datasets show that the proposed model signicantly outperforms current state-of-the-art methods. Our code and synthesized dataset will be made publicly available.
Semi-supervised FusedGAN for Conditional Image Generation
Authors: Gang Hua, Rama Chellappa Navaneeth Bodla
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Also another limitation of the existing methods is that they require fully supervised training data in the form of paired condition and image. For tasks such as text-to-image generation, the limited size of such fully supervised, paired training datasets may easily cause mode collapsing in training, which confronts the generation of diverse samples. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation.
Group Normalization
Authors: Kaiming He Yuxin Wu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code.
Conditional Image-Text Embedding Networks
Authors: Paige Kordas, M. Hadi Kiapour, Shuai Zheng, Robinson Piramuthu, Svetlana Lazebnik Bryan A. Plummer
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: This paper presents an approach for grounding phrases in images which jointly learns multiple text-conditioned embeddings in a single end-to-end model. In order to differentiate text phrases into semantically distinct subspaces, we propose a concept weight branch that automatically assigns phrases to embeddings, whereas prior works predefine such assignments. Our proposed solution simplifies the representation requirements for individual embeddings and allows the underrepresented concepts to take advantage of the shared representations before feeding them into concept-specific layers. Comprehensive experiments verify the effectiveness of our approach across three phrase grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a strong region-phrase embedding baseline.
Deep Co-Training for Semi-Supervised Image Recognition
Authors: Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille Siyuan Qiao
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework. The original Co-Training learns two classifiers on two views which are data from different sources that describe the same instances. To extend this concept to deep learning, Deep Co-Training trains multiple deep neural networks to be the different views and exploits adversarial examples to encourage view difference, in order to prevent the networks from collapsing into each other. As a result, the co-trained networks provide different and complementary information about the data, which is necessary for the Co-Training framework to achieve good results. We test our method on SVHN, CIFAR-10/100 and ImageNet datasets, and our method outperforms the previous state-of-the-art methods by a large margin.
Object Level Visual Reasoning in Videos
Authors: Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori Fabien Baradel
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Human activity recognition is typically addressed by training models to detect key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context. The next open challenges in activity recognition require a level of understanding that pushes beyond this, requiring fine distinctions and a detailed comprehension of the interactions between actors and objects in a scene. We propose a model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos. Key to our approach is the choice of performing this reasoning on an object level through the integration of state of the art object instance segmentation networks. This allows the model to learn detailed spatial interactions that exist at a semantic, object-interaction relevant level. We evaluated our method on three standard datasets: the Twenty-BN Something-Something dataset, the VLOG dataset and the EPIC Kitchens dataset, and achieve state of the art results on both. Finally, we also show visualizations of the interactions learned by the model, which illustrate object classes and their interactions corresponding to different activity classes.
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video
Authors: Miao Liu, James M. Rehg Yin Li
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera. We propose a novel deep model for joint gaze estimation and action recognition in First Person Vision. Our method describes the participant's gaze as a probabilistic variable and models its distribution using stochastic units in a deep network. We sample from these stochastic units to generate an attention map. This attention map guides the aggregation of visual features in action recognition, thereby providing coupling between gaze and action. We evaluate our method on the standard EGTEA dataset and demonstrate performance that exceeds the state-of-the-art by a significant margin of 3.5%.
Deep Factorised Inverse-Sketching
Authors: Da Li, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales Kaiyue Pang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Modelling human free-hand sketches has become topical recently, driven by practical applications such as fine-grained sketch based image retrieval (FG-SBIR). Sketches are clearly related to photo edge-maps, but a human free-hand sketch of a photo is not simply a clean rendering of that photo's edge map. Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included. In this paper we study this sketching process and attempt to invert it. We model this inversion by translating iconic free-hand sketches to contours that resemble more geometrically realistic projections of object boundaries, and separately factorise out the salient added details. This factorised re-representation makes it easier to match a free-hand sketch to a photo instance of an object. Specifically, we propose a novel unsupervised image style transfer model based on enforcing a cyclic embedding consistency constraint. A deep FG-SBIR model is then formulated to accommodate complementary discriminative detail from each factorised sketch for better matching with the corresponding photo. Our method is evaluated both qualitatively and quantitatively to demonstrate its superiority over a number of state-of-the-art alternatives for style transfer and FG-SBIR.
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
Authors: Jongseok Kim , Gunhee Kim Youngjae Yu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e.g. a video sequence and a language sentence). Our idea is to learn an effective multimodal matching network for sequence data, consisting of two key components. First the Joint Semantic Tensor embeds the joint representation of two sequence data, and then the Convolutional Hierarchical Decoder computes a matching score or predicts a word as an answer to a question, by discovering hidden hierarchical joint relations of two sequence modalities. Both modules leverage a attention mechanism to promote well-matched representation patterns while prune out misaligned ones in a bottom-up manner. Although the JSFusion is a universal model to be applicable to any multimodal sequence data, we focus on video-language tasks including multimodal retrieval and video QA. For evaluation of our JSFusion model, we evaluate our model in three VQA and retrieval tasks in LSMDC, for which our model achieves the best performance with significant margins. We also perform multiple-choice and movie retrieval tasks for the MSR-VTT dataset, on which our approach also outperforms many state-of-the-art methods.
View-graph Selection Framework for SfM
Authors: Visesh Chari, P J Narayanan Rajvi Shah
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: View-graph is an essential input to large-scale structure from motion (SfM) pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on the input view-graph. Inconsistent or inaccurate edges can lead to inferior or wrong reconstruction. Most SfM methods remove `undesirable' images and pairs using several fixed heuristic criteria, and propose tailor-made solutions to achieve specific reconstruction objectives such as efficiency, accuracy, or disambiguation. In contrast to these disparate solutions, we propose an optimization based formulation that can be used to achieve these different reconstruction objectives with task-specific cost modeling that uses and construct a very efficient network flow based formulation for its approximate solution. The abstraction brought on by this selection mechanism separates the challenges specific to datasets and reconstruction objectives from the standard SfM pipeline and improves its generalization. This paper mainly focuses on application of this framework with standard SfM pipeline for accurate and ghost-free reconstructions of highly ambiguous datasets. To model selection costs for this task, we introduce new disambiguation priors based on local geometry. We further demonstrate versatility of the method by using it for the general objective of accurate and efficient reconstruction of large-scale Internet datasets using costs based on well-known SfM priors.
Synthetically Supervised Feature Learning for Scene Text Recognition
Authors: Zhaowen Wang, Hailin Jin, Ian Wassell Yang Liu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: 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.
Deep Clustering for Unsupervised Learning of Visual Features
Authors: Piotr Bojanowski, Armand Joulin, Matthijs Douze Mathilde Caron
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large-scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
Authors: Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, Yupeng Gao Dong Su
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition. However, recent studies have highlighted the lack of robustness in well-trained deep neural networks to adversarial examples. Visually imperceptible perturbations to natural images can easily be crafted and mislead the image classifiers towards misclassification. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Our extensive experimental results reveal several new insights: (1) linear scaling law - the empirical $ell_2$ and $ell_infty$ distortion metrics scale linearly with the $log$ value of classification error; (2) model architecture is a more critical factor to robustness than model size, and the disclosed accuracy-robustness Pareto frontier can be used as an evaluation criterion for ImageNet model designers; (3) for a similar network architecture, increasing network depth slightly improves robustness in $ell_infty$ distortion; (4) there exist models (in VGG family) that exhibit high adversarial transferability, while most adversarial examples crafted from one model can only be transferred within the same family. Experiment code is publicly available at url{https://github.com/huanzhang12/Adversarial_Survey}.
Lifelong Learning via Progressive Distillation and Retrospection
Authors: Xinyu Pan, Chen Change Loy, Zilei Wang, Dahua Lin Saihui Hou
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Lifelong learning aims at adapting a learned model to new tasks while retaining the knowledge gained earlier. A key challenge for lifelong learning is how to strike a balance between the preservation on old tasks and the adaptation to a new one within a given model. Approaches that combine both objectives in training have been explored in previous works. Yet the performance still suffers from considerable degradation in a long sequence of tasks. In this work, we propose a novel approach to lifelong learning, which tries to seek a better balance between preservation and adaptation via two techniques: Distillation and Retrospection. Specifically, the target model adapts to the new task by knowledge distillation from an intermediate expert, while the previous knowledge is more effectively preserved by caching a small subset of data for old tasks. The combination of Distillation and Retrospection leads to a more gentle learning curve for the target model, and extensive experiments demonstrate that our approach can bring consistent improvements on both old and new tasks.
Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data
Authors: Quang-Trung Truong, Duc Thanh Nguyen, Jing Yu Koh, Lap-Fai Yu, Alexander Binder, Sai-Kit Yeung Tian Feng
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Urban zoning enables various applications in land use analysis and urban planning. As cities evolve, it is important to constantly update the zoning maps of cities to reflect urban pattern changes. This paper proposes a method for automatic urban zoning using higher-order Markov random fields (HO-MRF) built on multi-view imagery data including street-view photos and top-view satellite images. In the proposed HO-MRF, top-view satellite data is segmented via a multi-scale deep convolutional neural network (MS-CNN) and used in lower-order potentials. Street-view data with geo-tagged information is augmented in higher-order potentials. Various feature types for classifying street-view images were also investigated in our work. We evaluated the proposed method on a number of famous metropolises and provided in-depth analysis on technical issues.
Progressive Neural Architecture Search
Authors: Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy Chenxi Liu
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute. The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.
Single Image Water Hazard Detection using FCN with Reflection Attention Units
Authors: Chuong Nguyen, Shaodi You, Jianfeng Lu Xiaofeng Han
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Water bodies, such as puddles and flooded areas, on and off road pose significant risks to autonomous cars. Detecting water from moving camera is a challenging task as water surface is highly refractive, and its appearance varies with viewing angle, surrounding scene, weather conditions. In this paper, we present a water puddle detection method based on a Fully Convolutional Network (FCN) with our newly proposed Reflection Attention Units (RAUs). An RAU is a deep network unit designed to embody the physics of reflection on water surface from sky and nearby scene. To verify the performance of our proposed method, we collect 11455 color stereo images with polarizers, and 985 of left images are annotated and divided into 2 datasets: On Road (ONR) dataset and Off Road (OFR) dataset. We show that FCN-8s with RAUs improves significantly precision and recall metrics as compared to FCN-8s, DeepLab V2 and Gaussian Mixture Model (GMM). We also show that focal loss function can improve the performance of FCN-8s network due to the extreme imbalance of water versus ground classification problem.
Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition
Authors: Minjie Cai, Zhenqiang Li, Yoichi Sato Yifei Huang
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks. Our assumption is that the high-level context of how a task is completed in a certain way has a strong influence on attention transition and should be modeled for gaze prediction in natural dynamic scenes. Specifically, we propose a hybrid model based on deep neural networks which integrates task-dependent attention transition with bottom-up saliency prediction. In particular, the task-dependent attention transition is learned with a recurrent neural network to exploit the temporal context of gaze fixations, e.g., looking at a cup after moving gaze away from a grasped bottle. Experiments on public egocentric activity datasets show that our model significantly outperforms state-of-the-art gaze prediction methods and is able to learn meaningful transition of human attention.
Joint Learning of Intrinsic Images and Semantic Segmentation
Authors: Thomas T. Groenestege, Partha Das, Hoang-An Le, Sezer Karaoglu, Theo Gevers Anil S. Baslamisli
Published in: European Conference on Computer Vision (ECCV), 2018 (Main Conference Track), 2018

Abstract: Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinseg