mtybilly/Instruct2DS huggingface dataset:
Taskonomy: Disentangling Task Transfer Learning
Authors: Amir R. Zamir, Alexander Sax, William Shen, Leonidas J. Guibas, Jitendra Malik, Silvio Savarese
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable uses; it is the concept underlying transfer learning and, for example, can provide a principled way for reusing supervision among related tasks, finding what tasks transfer well to an arbitrary target task, or solving many tasks in one system without piling up the complexity.      This paper proposes a fully computational approach for finding the structure of the space of visual tasks. This is done via a sampled dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks, and modeling their (1st and higher order) transfer dependencies in a latent space. The product can be viewed as a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. the nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 while keeping the performance nearly the same. Users can employ a provided Binary Integer Programming solver that leverages the taxonomy to find efficient supervision policies for their own use cases.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Authors: Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, Tatsuya Harada
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this work, we present a method for unsupervised domain adaptation.  Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics.  To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.  We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy.  Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at url{https://github.com/mil-tokyo/MCD_DA}
Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Authors: Zhirong Wu, Yuanjun Xiong, Stella X. Yu, Dahua Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsu- pervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time.
Multi-Task Adversarial Network for Disentangled Feature Learning
Authors: Yang Liu, Zhaowen Wang, Hailin Jin, Ian Wassell
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discriminators are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction. The generator provides further regularization on the learned feature by reconstructing images with shared factors as the input image. We design a new optimization scheme to stabilize the adversarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outperforms the state-of-the-art methods in terms of both recognizing the factors of interest and generalization to images with unseen variations.
Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Authors: Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Visual Domain Adaptation is a problem of immense importance in computer vision. Previous approaches showcase the inability of even deep neural networks to learn informative representations across domain shift. This problem is more severe for tasks where acquiring hand labeled data is extremely hard and tedious. In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains. Contrary to previous approaches that use a simple adversarial objective or superpixel information to aid the process, we propose an approach based on Generative Adversarial Networks (GANs) that brings the embeddings closer in the learned feature space. To showcase the generality and scalability of our approach, we show that we can achieve state of the art results on two challenging scenarios of synthetic to real domain adaptation. Additional exploratory experiments show that our approach: (1) generalizes to unseen domains and (2) results in improved alignment of source and target distributions.
Empirical Study of the Topology and Geometry of Deep Networks
Authors: Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: The goal of this paper is to analyze the geometric properties of deep neural network image classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical study, we show that state-of-the-art deep nets learn connected classification regions, and that the decision boundary in the vicinity of datapoints is flat along most directions. We further draw an essential connection between two seemingly unrelated properties of deep networks: their sensitivity to additive perturbations of the inputs, and the curvature of their decision boundary. The directions where the decision boundary is curved in fact characterize the directions to which the classifier is the most vulnerable. We finally leverage a fundamental asymmetry in the curvature of the decision boundary of deep nets, and propose a method to discriminate between original images, and images perturbed with small adversarial examples. We show the effectiveness of this purely geometric approach for detecting small adversarial perturbations in images, and for recovering the labels of perturbed images.
Boosting Domain Adaptation by Discovering Latent Domains
Authors: Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases exploiting single-source DA methods for learning target classifiers may lead to sub-optimal, if not poor, results. In addition, in many applications it is difficult to manually provide the domain labels for all source data points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this information to learn robust target classifiers. Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We test our approach on publicly-available datasets, showing that it outperforms state-of-the-art multi-source DA methods by a large margin.
Shape From Shading Through Shape Evolution
Authors: Dawei Yang, Jia Deng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark.
Weakly Supervised Instance Segmentation Using Class Peak Response
Authors: Yanzhao Zhou, Yi Zhu, Qixiang Ye, Qiang Qiu, Jianbin Jiao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable a classification network for instance mask extraction. With image labels supervision only, CNN classifiers in a fully convolutional manner can produce class response maps, which specify classification confidence at each image location. We observed that local maximums, i.e., peaks, in a class response map typically correspond to strong visual cues residing inside each instance. Motivated by this, we first design a process to stimulate peaks to emerge from a class response map. The emerged peaks are then back-propagated and effectively mapped to highly informative regions of each object instance, such as instance boundaries. We refer to the above maps generated from class peak responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed instance-level representation, which allows instance masks to be extracted even with some off-the-shelf methods. To the best of our knowledge, we for the first time report results for the challenging image-level supervised instance segmentation task. Extensive experiments show that our method also boosts weakly supervised pointwise localization as well as semantic segmentation performance, and reports state-of-the-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO.
Collaborative and Adversarial Network for Unsupervised Domain Adaptation
Authors: Weichen Zhang, Wanli Ouyang, Wen Li, Dong Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain-adversarial training of neural networks. We use several domain classifiers on multiple CNN feature extraction layers/blocks, in which each domain classifier is connected to the hidden representations from one block and one loss function is defined based on the hidden presentation and the domain labels (e.g., source and target). We design a new loss function by integrating the losses from all blocks in order to learn informative representations from lower layers through collaborative learning and learn uninformative representations from higher layers through adversarial learning. We further extend our CAN method as Incremental CAN (iCAN), in which we iteratively select a set of pseudo-labelled target samples based on the image classifier and the last domain classifier from the previous training epoch and re-train our CAN model using the enlarged training set.  Comprehensive experiments on two benchmark datasets Office and ImageCLEF-DA clearly demonstrate the effectiveness of our newly proposed approaches CAN and iCAN for unsupervised domain adaptation.
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines
Authors: Shuqin Xie, Zitian Chen, Chao Xu, Cewu Lu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recent advances in multi-stage algorithms have shown great promise, but two important problems still remain. First of all, at inference time, information can't feed back from downstream to upstream. Second, at training time, end-to-end training is not possible if the overall pipeline involves non-differentiable functions, and so different stages can't be jointly optimized. In this paper, we propose a novel environment upgrade reinforcement learning framework to solve the feedback and joint optimization problems. Our framework re-links the downstream stage to the upstream stage by a reinforcement learning agent. While training the agent to improve final performance by refining the upstream stage's output, we also upgrade the downstream stage (environment) according to the agent's policy. In this way, agent policy and environment are jointly optimized. We propose a training algorithm for this framework to address the different training demands of agent and environment. Experiments on instance segmentation and human pose estimation demonstrate the effectiveness of the proposed framework.
Teaching Categories to Human Learners With Visual Explanations
Authors: Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, Yisong Yue
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We study the problem of computer-assisted teaching with explanations.   Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance.   However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept.  To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information.   In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods.
Density Adaptive Point Set Registration
Authors: Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssén, Michael Felsberg
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets. We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.
Left-Right Comparative Recurrent Model for Stereo Matching
Authors: Zequn Jie, Pengfei Wang, Yonggen Ling, Bo Zhao, Yunchao Wei, Jiashi Feng, Wei Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Leveraging the disparity information from both  left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view. However, the conventional left-right consistency check is an isolated post-processing step and heavily hand-crafted. This paper proposes a novel left-right comparative recurrent model to perform left-right consistency checking jointly with   disparity estimation. At each recurrent step, the model produces disparity results for both views, and then performs online left-right comparison to identify the mismatched regions which may probably contain erroneously labeled pixels. A soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step. In this way, the generated disparity maps are progressively improved by the proposed recurrent model. Extensive evaluations on  KITTI 2015, Scene Flow and Middlebury benchmarks validate the effectiveness of our model,  demonstrating that state-of-the-art stereo disparity estimation results can be achieved by this new model.
Im2Pano3D: Extrapolating 360° Structure and Semantics Beyond the Field of View
Authors: Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, Thomas Funkhouser
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic view of an indoor scene when given only a partial observation ( <=50%) in the form of an RGB-D image. To make this possible, Im2Pano3D leverages strong contextual priors learned from large-scale synthetic and real-world indoor scenes. To ease the prediction of 3D structure, we propose to parameterize 3D surfaces with their plane equations and train the model to predict these parameters directly. To provide meaningful training supervision, we make use of multiple loss functions that consider both pixel level accuracy and global context consistency. Experiments demonstrate that Im2Pano3D is able to predict the semantics and 3D structure of the unobserved scene with more than 56% pixel accuracy and less than 0.52m average distance error, which is significantly better than alternative approaches.
Polarimetric Dense Monocular SLAM
Authors: Luwei Yang, Feitong Tan, Ao Li, Zhaopeng Cui, Yasutaka Furukawa, Ping Tan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera. The algorithm exploits both photometric and polarimetric light information to produce more accurate and complete geometry. The polarimetric information allows us to recover the azimuth angle of surface normals from each video frame to facilitate dense reconstruction, especially at textureless or specular regions. There are two challenges in our approach: 1) surface azimuth angles from the polarization camera are very noisy; and 2) we need a near real-time solution for SLAM. Previous successful methods on polarimetric multi-view stereo are offline and require manually pre-segmented object masks to suppress the effects of erroneous angle information along boundaries. Our fully automatic approach efficiently iterates azimuth-based depth propagations, two-view depth consistency check, and depth optimization to produce a depthmap in real-time, where all the algorithmic steps are carefully designed to enable a GPU implementation. To our knowledge, this paper is the first to propose a photometric and polarimetric method for dense SLAM. We have qualitatively and quantitatively evaluated our algorithm against a few of competing methods, demonstrating the superior performance on various indoor and outdoor scenes.
A Unifying Contrast Maximization Framework for Event Cameras, With Applications to Motion, Depth, and Optical Flow Estimation
Authors: Guillermo Gallego, Henri Rebecq, Davide Scaramuzza
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the event data by maximizing an objective function: the contrast of an image of warped events. Our method implicitly handles data association between the events, and therefore, does not rely on additional appearance information about the scene. In addition to accurately recovering the motion parameters of the problem, our framework produces motion-corrected edge-like images with high dynamic range that can be used for further scene analysis. The proposed method is not only simple, but more importantly, it is, to the best of our knowledge, the first method that can be successfully applied to such a diverse set of important vision tasks with event cameras.
Modeling Facial Geometry Using Compositional VAEs
Authors: Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose a method for learning non-linear face geometry  representations using deep generative models.  Our model is a variational autoencoder with multiple levels of  hidden variables where lower layers capture global geometry  and higher ones encode more local deformations.  Based on that, we propose a new parameterization of facial  geometry that naturally decomposes the structure of the human face  into a set of semantically meaningful levels of detail.  This parameterization enables us to do model fitting while  capturing varying level of detail under different types of geometrical  constraints.
Tangent Convolutions for Dense Prediction in 3D
Authors: Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data. In contrast to volumetric approaches, our method operates directly on surface geometry. Crucially, the construction is applicable to unstructured point clouds and other noisy real-world data. We show that tangent convolutions can be evaluated efficiently on large-scale point clouds with millions of points. Using tangent convolutions, we design a deep fully-convolutional network for semantic segmentation of 3D point clouds, and apply it to challenging real-world datasets of indoor and outdoor 3D environments. Experimental results show that the presented approach outperforms other recent deep network constructions in detailed analysis of large 3D scenes.
RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials
Authors: Despoina Paschalidou, Osman Ulusoy, Carolin Schmitt, Luc Van Gool, Andreas Geiger
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occlusion. Instead, classical approaches based on Markov Random Fields (MRF) with ray-potentials explicitly model these physical processes, but they cannot cope with large surface appearance variations across different viewpoints. In this paper, we propose RayNet, which combines the strengths of both frameworks. RayNet integrates a CNN that learns view-invariant feature representations with an MRF that explicitly encodes the physics of perspective projection and occlusion. We train RayNet end-to-end using empirical risk minimization. We thoroughly evaluate our approach on challenging real-world datasets and demonstrate its benefits over a piece-wise trained baseline, hand-crafted models as well as other learning-based approaches.
Neural 3D Mesh Renderer
Authors: Hiroharu Kato, Yoshitaka Ushiku, Tatsuya Harada
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Authors: Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, Elisa Ricci
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce a novel approach for monocular depth estimation. Similarly to previous works, our method employs a continuous CRF to fuse multi-scale information derived from different layers of a front-end Convolutional Neural Network (CNN). Differently from past works, our approach benefits from a structured attention model which automatically regulates the amount of information transferred between corresponding features at different scales. Importantly, the proposed attention model is seamlessly integrated into the CRF, allowing end-to-end training of the entire architecture. Our extensive experimental evaluation demonstrates the effectiveness of the proposed method which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.
Automatic 3D Indoor Scene Modeling From Single Panorama
Authors: Yang Yang, Shi Jin, Ruiyang Liu, Sing Bing Kang, Jingyi Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We describe a system that automatically extracts 3D geometry of an indoor scene from a single 2D panorama. Our system recovers the spatial layout by finding the floor, walls, and ceiling; it also recovers shapes of typical indoor objects such as furniture. Using sampled perspective sub-views, we extract geometric cues (lines, vanishing points, orientation map, and surface normals) and semantic cues (saliency and object detection information). These cues are used for ground plane estimation and occlusion reasoning. The global spatial layout is inferred through a constraint graph on line segments and planar superpixels. The recovered layout is then used to guide shape estimation of the remaining objects using their normal information. Experiments on synthetic and real datasets show that our approach is state-of-the-art in both accuracy and efficiency. Our system can handle cluttered scenes with complex geometry that are challenging to existing techniques.
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Authors: Anh Tuấn Trần, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, Gérard Medioni
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Existing single view, 3D face reconstruction methods can produce beautifully detailed 3D results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide detailed 3D reconstructions of faces viewed under extreme conditions, out of plane rotations, and occlusions. Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which acts as a foundation and then separately layer this foundation with details represented by a bump map. We show how a deep convolutional encoder-decoder can be used to estimate such bump maps. We further show how this approach naturally extends to generate plausible details for occluded facial regions. We test our approach and its components extensively, quantitatively demonstrating the invariance of our estimated facial details. We further provide numerous qualitative examples showing that our method produces detailed 3D face shapes in viewing conditions where existing state of the art often break down.
Beyond Grobner Bases: Basis Selection for Minimal Solvers
Authors: Viktor Larsson, Magnus Oskarsson, Kalle Astrom, Alge Wallis, Zuzana Kukelova, Tomas Pajdla
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have traditionally been based on a Grobner basis for the polynomial ideal. Here we describe how we can enumerate all such bases in an efficient way. We also show that going beyond Grobner bases leads to more efficient solvers in many cases. We present a novel basis sampling scheme that we evaluate on a number of problems.
Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape From Images
Authors: Silvia Zuffi, Angjoo Kanazawa, Michael J. Black
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.
Deep Cocktail Network: Multi-Source Unsupervised Domain Adaptation With Category Shift
Authors: Ruijia Xu, Ziliang Chen, Wangmeng Zuo, Junjie Yan, Liang Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Most existing unsupervised domain adaptation (UDA) methods are based upon the assumption that source labeled data come from an identical underlying distribution. Whereas in practical scenario, labeled instances are typically collected from diverse sources. Moreover, those sources may not completely share their categories, which further brings a category shift challenge to multi-source (unsupervised) domain adaptation (MDA). In this paper, we propose a deep cocktail network (DCTN), to battle the domain and category shifts among multiple sources. Motivated by the theoretical results in cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the training of MDA via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains. ii) The multi-source category classifiers are integrated with the perplexity scores to classify target sample, and the pseudo-labeled target samples together with source samples are utilized to update the multi-source category classifier and the representation module. We evaluate DCTN in three domain adaptation benchmarks, which clearly demonstrate the superiority of our framework.
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Authors: Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA).  To this end, we collect 2806 aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using 15 common object categories. The fully annotated DOTA images contains 188,282 instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral. To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.
Finding Beans in Burgers: Deep Semantic-Visual Embedding With Localization
Authors: Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic relationships. Such a multi-modal embedding can be trained and used for various tasks, notably image captioning. In the present work, we introduce a new architecture of this type, with a visual path that leverages recent space-aware pooling mechanisms. Combined with a textual path which is jointly trained from scratch, our semantic-visual embedding offers a versatile model. Once trained under the supervision of captioned images, it yields new state-of-the-art performance on cross-modal retrieval. It also allows the localization of new concepts from the embedding space into any input image, delivering state-of-the-art result on the visual grounding of phrases.
Feature Super-Resolution: Make Machine See More Clearly
Authors: Weimin Tan, Bo Yan, Bahetiyaer Bare
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Identifying small size images or small objects is a notoriously challenging problem, as discriminative representations are difficult to learn from the limited information contained in them with poor-quality appearance and unclear object structure. Existing research works usually increase the resolution of low-resolution image in the pixel space in order to provide better visual quality for human viewing. However, the improved performance of such methods is usually limited or even trivial in the case of very small image size (we will show it in this paper explicitly).  In this paper, different from image super-resolution (ISR), we propose a novel super-resolution technique called feature super-resolution (FSR), which aims at enhancing the discriminatory power of small size image in order to provide high recognition precision for machine. To achieve this goal, we propose a new Feature Super-Resolution Generative Adversarial Network (FSR-GAN) model that transforms the raw poor features of small size images to highly discriminative ones by performing super-resolution in the feature space. Our FSR-GAN consists of two subnetworks: a feature generator network G and a feature discriminator network D. By training the G and the D networks in an alternative manner, we encourage the G network to discover the latent distribution correlations between small size and large size images and then use G to improve the representations of small images. Extensive experiment results on Oxford5K, Paris, Holidays, and Flick100k datasets demonstrate that the proposed FSR approach can effectively enhance the discriminatory ability of features. Even when the resolution of query images is reduced greatly, e.g., 1/64 original size, the query feature enhanced by our FSR approach achieves surprisingly high retrieval performance at different image resolutions and increases the retrieval precision by 25% compared to the raw query feature.
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Authors: Rodney LaLonde, Dong Zhang, Mubarak Shah
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Object detection in wide area motion imagery (WAMI) has drawn the attention of the computer vision research community for a number of years. WAMI proposes a number of unique challenges including extremely small object sizes, both sparse and densely-packed objects, and extremely large search spaces (large video frames). Nearly all state-of-the-art methods in WAMI object detection report that appearance-based classifiers fail in this challenging data and instead rely almost entirely on motion information in the form of background subtraction or frame-differencing. In this work, we experimentally verify the failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a heatmap-based fully convolutional neural network (CNN), and propose a novel two-stage spatio-temporal CNN which effectively and efficiently combines both appearance and motion information to significantly surpass the state-of-the-art in WAMI object detection. To reduce the large search space, the first stage (ClusterNet) takes in a set of extremely large video frames, combines the motion and appearance information within the convolutional architecture, and proposes regions of objects of interest (ROOBI). These ROOBI can contain from one to clusters of several hundred objects due to the large video frame size and varying object density in WAMI. The second stage (FoveaNet) then estimates the centroid location of all objects in that given ROOBI simultaneously via heatmap estimation. The proposed method exceeds state-of-the-art results on the WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped objects, as well as being the first proposed method in wide area motion imagery to detect completely stationary objects.
MaskLab: Instance Segmentation by Refining Object Detection With Semantic and Direction Features
Authors: Liang-Chieh Chen, Alexander Hermans, George Papandreou, Florian Schroff, Peng Wang, Hartwig Adam
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (eg, atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.
Hashing as Tie-Aware Learning to Rank
Authors: Kun He, Fatih Cakir, Sarah Adel Bargal, Stan Sclaroff
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.
Classification-Driven Dynamic Image Enhancement
Authors: Vivek Sharma, Ali Diba, Davy Neven, Michael S. Brown, Luc Van Gool, Rainer Stiefelhagen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content.  Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer.  In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.  To this end, we present a unified CNN architecture that uses a range of enhancement filters that can enhance  image-specific details via end-to-end dynamic filter learning. We demonstrate the effectiveness of this strategy on four challenging benchmark  datasets for fine-grained, object, scene and texture classification: CUB-200-2011, PASCAL-VOC2007,  MIT-Indoor, and DTD. Experiments using our proposed enhancement  shows promising results on all the datasets. In addition, our approach is capable of improving the performance of all generic CNN architectures.
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding
Authors: Kan Chen, Jiyang Gao, Ram Nevatia
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supervised scenario, mapping between image regions (i.e., proposals) and language is not available in the training set. Previous methods address this deficiency by training a grounding system via learning to reconstruct language information contained in input queries from predicted proposals. However, the optimization is solely guided by the reconstruction loss from the language modality, and ignores rich visual information contained in proposals and useful cues from external knowledge. In this paper, we explore the consistency contained in both visual and language modalities, and leverage complementary external knowledge to facilitate weakly supervised grounding. We propose a novel Knowledge Aided Consistency Network (KAC Net) which is optimized by reconstructing input query and proposal's information. To leverage complementary knowledge contained in the visual features, we introduce a Knowledge Based Pooling (KBP) gate to focus on query-related proposals. Experiments show that KAC Net provides a significant improvement on two popular datasets.