mtybilly/Instruct2DS huggingface dataset:
TransGaGa: Geometry-Aware Unsupervised Image-To-Image Translation
Authors: Wayne Wu, Kaidi Cao, Cheng Li, Chen Qian, Chen Change Loy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Unsupervised image-to-image translation aims at learning a mapping between two visual domains. However, learning a translation across large geometry variations al- ways ends up with failure. In this work, we present a novel disentangle-and-translate framework to tackle the complex objects image-to-image translation task. Instead of learning the mapping on the image space directly, we disentangle image space into a Cartesian product of the appearance and the geometry latent spaces. Specifically, we first in- troduce a geometry prior loss and a conditional VAE loss to encourage the network to learn independent but com- plementary representations. The translation is then built on appearance and geometry space separately. Extensive experiments demonstrate the superior performance of our method to other state-of-the-art approaches, especially in the challenging near-rigid and non-rigid objects translation tasks. In addition, by taking different exemplars as the ap- pearance references, our method also supports multimodal translation. Project page: https://wywu.github. io/projects/TGaGa/TGaGa.html
Depth-Attentional Features for Single-Image Rain Removal
Authors: Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Pheng-Ann Heng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks. Existing methods and datasets for rain removal, however, ignore these physical properties, thereby limiting the rain removal efficiency on real photos. In this work, we first analyze the visual effects of rain subject to scene depth and formulate a rain imaging model collectively with rain streaks and fog; by then, we prepare a new dataset called RainCityscapes with rain streaks and fog on real outdoor photos. Furthermore, we design an end-to-end deep neural network, where we train it to learn depth-attentional features via a depth-guided attention mechanism, and regress a residual map to produce the rain-free image output. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others.
Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior
Authors: Lizhi Wang, Chen Sun, Ying Fu, Min H. Kim, Hua Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Regularization is a fundamental technique to solve an ill-posed optimization problem robustly and is essential to reconstruct compressive hyperspectral images. Various hand-crafted priors have been employed as a regularizer but are often insufficient to handle the wide variety of spectra of natural hyperspectral images, resulting in poor reconstruction quality. Moreover, the prior-regularized optimization requires manual tweaking of its weight parameters to achieve a balance between the spatial and spectral fidelity of result images. In this paper, we present a novel hyperspectral image reconstruction algorithm that substitutes the traditional hand-crafted prior with a data-driven prior, based on an optimization-inspired network. Our method consists of two main parts: First, we learn a novel data-driven prior that regularizes the optimization problem with a goal to boost the spatial-spectral fidelity. Our data-driven prior learns both local coherence and dynamic characteristics of natural hyperspectral images. Second, we combine our regularizer with an optimization-inspired network to overcome the heavy computation problem in the traditional iterative optimization methods. We learn the complete parameters in the network through end-to-end training, enabling robust performance with high accuracy. Extensive simulation and hardware experiments validate the superior performance of our method over the state-of-the-art methods.
LiFF: Light Field Features in Scale and Depth
Authors: Donald G. Dansereau, Bernd Girod, Gordon Wetzstein
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Feature detectors and descriptors are key low-level vision tools that many higher-level tasks build on. Unfortunately these fail in the presence of challenging light transport effects including partial occlusion, low contrast, and reflective or refractive surfaces. Building on spatio-angular imaging modalities offered by emerging light field cameras, we introduce a new and computationally efficient 4D light field feature detector and descriptor: LiFF. LiFF is scale invariant and utilizes the full 4D light field to detect features that are robust to changes in perspective. This is particularly useful for structure from motion (SfM) and other tasks that match features across viewpoints of a scene. We demonstrate significantly improved 3D reconstructions via SfM when using LiFF instead of the leading 2D or 4D features, and show that LiFF runs an order of magnitude faster than the leading 4D approach. Finally, LiFF inherently estimates depth for each feature, opening a path for future research in light field-based SfM.
Deep Exemplar-Based Video Colorization
Authors: Bo Zhang, Mingming He, Jing Liao, Pedro V. Sander, Lu Yuan, Amine Bermak, Dong Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper presents the first end-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively.
On Finding Gray Pixels
Authors: Yanlin Qian, Joni-Kristian Kamarainen, Jarno Nikkanen, Jiri Matas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation.  The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in  0.4 seconds with a non-optimized Matlab code.
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
Authors: Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang, Wei Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption. UnOS significantly outperforms other state-of-the-art (SOTA) unsupervised approaches that treated the two tasks independently. Specifically, given two consecutive stereo image pairs from a video, UnOS estimates per-pixel stereo depth images, camera ego-motion and optical flow with three parallel CNNs. Based on these quantities, UnOS computes rigid optical flow and compares it against the optical flow estimated from the FlowNet, yielding pixels satisfying the rigid-scene assumption. Then, we encourage geometrical consistency between the two estimated flows within rigid regions, from which we derive a rigid-aware direct visual odometry (RDVO) module. We also propose rigid and occlusion-aware flow-consistency losses for the learning of UnOS. We evaluated our results on the popular KITTI dataset over 4 related tasks, i.e. stereo depth, optical flow, visual odometry and motion segmentation.
Learning Transformation Synchronization
Authors: Xiangru Huang, Zhenxiao Liang, Xiaowei Zhou, Yao Xie, Leonidas J. Guibas, Qixing Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Reconstructing the 3D model of a physical object typically requires us to align the depth scans obtained from different camera poses into the same coordinate system. Solutions to this global alignment problem usually proceed in two steps. The first step estimates relative transformations between pairs of scans using an off-the-shelf technique. Due to limited information presented between pairs of scans, the resulting relative transformations are generally noisy. The second step then jointly optimizes the relative transformations among all input depth scans. A natural constraint used in this step is the cycle-consistency constraint, which allows us to prune incorrect relative transformations by detecting inconsistent cycles. The performance of such approaches, however, heavily relies on the quality of the input relative transformations. Instead of merely using the relative transformations as the input to perform transformation synchronization, we propose to use a neural network to learn the weights associated with each relative transformation. Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network. We demonstrate the usefulness of this approach across a wide range of datasets.
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Authors: Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, Torsten Sattler
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level structures. We show that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations. The proposed method obtains state-of-the-art performance on both the difficult Aachen Day-Night localization dataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarks for image matching and 3D reconstruction.
Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring
Authors: Seungjun Nah, Sanghyun Son, Kyoung Mu Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Recurrent neural networks (RNNs) are widely used for sequential data processing. Recent state-of-the-art video deblurring methods bank on convolutional recurrent neural network architectures to exploit the temporal relationship between neighboring frames. In this work, we aim to improve the accuracy of recurrent models by adapting the hidden states transferred from past frames to the frame being processed so that the relations between video frames could be better used. We iteratively update the hidden state via re-using RNN cell parameters before predicting an output deblurred frame. Since we use existing parameters to update the hidden state, our method improves accuracy without additional modules. As the architecture remains the same regardless of iteration number, fewer iteration models can be considered as a partial computational path of the models with more iterations. To take advantage of this property, we employ a stochastic method to optimize our iterative models better. At training time, we randomly choose the iteration number on the fly and apply a regularization loss that favors less computation unless there are considerable reconstruction gains. We show that our method exhibits state-of-the-art video deblurring performance while operating in real-time speed.
Learning to Extract Flawless Slow Motion From Blurry Videos
Authors: Meiguang Jin, Zhe Hu, Paolo Favaro
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper, we introduce the task of generating a sharp slow-motion video given a low frame rate blurry video.  We propose a data-driven approach, where the training data is captured with a high frame rate camera and blurry images are simulated through an averaging process.  While it is possible to train a neural network to recover the sharp frames from their average, there is no guarantee of the temporal smoothness for the formed video, as the frames are estimated independently. To address the temporal smoothness requirement we propose a system with two networks: One, DeblurNet, to predict sharp keyframes  and the second, InterpNet, to predict intermediate frames between the generated keyframes. A smooth transition is ensured by interpolating between consecutive keyframes using InterpNet.  Moreover, the proposed scheme enables further increase in frame rate without retraining the network, by applying InterpNet recursively between pairs of sharp frames. We evaluate the proposed method on several datasets, including a novel dataset captured with a Sony RX V camera. We also demonstrate its performance of increasing the frame rate up to 20 times on real blurry videos.
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination
Authors: Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures. However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural. Therefore, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result. In particular, we focus on the domain prior properties of SISR problem. Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.
RF-Net: An End-To-End Image Matching Network Based on Receptive Field
Authors: Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, Zijian He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable matching framework is desirable and challenging. The very recent approach, LF-Net, successfully embeds the entire feature extraction pipeline into a jointly trainable pipeline, and produces the state-of-the-art matching results. This paper introduces two modifications to the structure of LF-Net. First, we propose to construct receptive feature maps, which lead to more effective keypoint detection. Second, we introduce a general loss function term, neighbor mask, to facilitate training patch selection. This results in improved stability in descriptor training. We trained RF-Net on the open dataset HPatches, and compared it with other methods on multiple benchmark datasets. Experiments show that RF-Net outperforms existing state-of-the-art methods.
Fast Single Image Reflection Suppression via Convex Optimization
Authors: Yang Yang, Wenye Ma, Yin Zheng, Jian-Feng Cai, Weiyu Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Removing undesired reflections from images taken through the glass is of great importance in computer vision. It serves as a means to enhance the image quality for aesthetic purposes as well as to preprocess images in machine learning and pattern recognition applications. We propose a convex model to suppress the reflection from a single input image. Our model implies a partial differential equation with gradient thresholding, which is solved efficiently using Discrete Cosine Transform. Extensive experiments on synthetic and real-world images demonstrate that our approach achieves desirable reflection suppression results and dramatically reduces the execution time compared to the state of the art.
A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision
Authors: Runmin Wu, Mengyang Feng, Wenlong Guan, Dong Wang, Huchuan Lu, Errui Ding
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we propose to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection. First, we leverage salient object detection and foreground contour detection tasks in an intertwined manner to generate saliency maps with uniform highlight. Second, the foreground contour and edge detection tasks guide each other simultaneously, thereby leading to preciser foreground contour prediction and reducing the local noises for edge prediction. In addition, we develop a novel mutual learning module (MLM) which serves as the building block of our method. Each MLM consists of multiple network branches trained in a mutual learning manner, which improves the performance by a large margin. Extensive experiments on seven challenging datasets demonstrate that the proposed method has delivered state-of-the-art results in both salient object detection and edge detection.
Enhanced Pix2pix Dehazing Network
Authors: Yanyun Qu, Yizi Chen, Jingying Huang, Yuan Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper,  we reduce the image dehazing problem to an image-to-image translation problem, and propose  Enhanced Pix2pix Dehazing Network (EPDN),  which generates a haze-free image without relying on the physical scattering model.  EPDN is embedded by a generative adversarial network,  which is followed by a well-designed enhancer.  Inspired by visual perception global-first theory,  the discriminator guides the generator to create a pseudo realistic image on a coarse scale,  while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale.  The enhancer contains two enhancing blocks based on the receptive field model,  which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trained with the enhancer. Extensive experiment results on synthetic datasets and real-world datasets show that the proposed EPDN is superior to the state-of-the-art methods in terms of PSNR,  SSIM,   PI, and subjective visual effect.
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements
Authors: Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, Hua Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems. Although state-of-the-art methods can obtain decent results in certain situations, performance declines significantly when tackling more general real-world cases.  These failures stem from the intrinsic difficulty of single image reflection removal -- the fundamental ill-posedness of the problem, and the insufficiency of densely-labeled training data needed for resolving this ambiguity within learning-based neural network pipelines. In this paper, we address these issues by exploiting targeted network enhancements and the novel use of misaligned data. For the former, we augment a baseline network architecture by embedding context encoding modules that are capable of leveraging high-level contextual clues to reduce indeterminacy within areas containing strong reflections. For the latter, we introduce an alignment-invariant loss function that facilitates exploiting misaligned real-world training data that is much easier to collect.  Experimental results collectively show that our method outperforms the state-of-the-art with aligned data, and that significant improvements are possible when using additional misaligned data.
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation
Authors: Wenbin Wang, Ruiping Wang, Shiguang Shan, Xilin Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Relationship is the core of scene graph, but its prediction is far from satisfying because of its complex visual diversity. To alleviate this problem, we treat relationship as an abstract object, exploring not only significative visual pattern but contextual information for it, which are two key aspects when considering object recognition. Our observation on current datasets reveals that there exists intimate association among relationships. Therefore, inspired by the successful application of context to object-oriented tasks, we especially construct context for relationships where all of them are gathered so that the recognition could benefit from their association. Moreover, accurate recognition needs discriminative visual pattern for object, and so does relationship. In order to discover effective pattern for relationship, traditional relationship feature extraction methods such as using union region or combination of subject-object feature pairs are replaced with our proposed intersection region which focuses on more essential parts. Therefore, we present our so-called Relationship Context - InterSeCtion Region (CISC) method. Experiments for scene graph generation on Visual Genome dataset and visual relationship prediction on VRD dataset indicate that both the relationship context and intersection region improve performances and realize anticipated functions.
Learning From Synthetic Data for Crowd Counting in the Wild
Authors: Qi Wang, Junyu Gao, Wei Lin, Yuan Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations. Extensive experiments show that the first method achieves the state-of-the-art performance on four real datasets, and the second outperforms our baselines. The dataset and source code are available at https://gjy3035.github.io/GCC-CL/.
A Local Block Coordinate Descent Algorithm for the CSC Model
Authors: Ev Zisselman, Jeremias Sulam, Michael Elad
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities. By providing a global, yet tractable, model that operates on the whole image, the CSC was shown to overcome several limitations of the patch-based sparse model while achieving superior performance in various applications. Contemporary methods for pursuit and learning the CSC dictionary often rely on the Alternating Direction Method of Multipliers (ADMM) in the Fourier domain for the computational convenience of convolutions, while ignoring the local characterizations of the image. In this work we propose a new and simple approach that adopts a localized strategy, based on the Block Coordinate Descent algorithm. The proposed method, termed Local Block Coordinate Descent (LoBCoD), operates locally on image patches. Furthermore, we introduce a novel stochastic gradient descent version of LoBCoD for training the convolutional filters. This Stochastic-LoBCoD leverages the benefits of online learning, while being applicable even to a single training image. We demonstrate the advantages of the proposed algorithms for image inpainting and multi-focus image fusion, achieving state-of-the-art results.
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation
Authors: Rakshith Shetty, Bernt Schiele, Mario Fritz
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Importance of visual context in scene understanding tasks is well recognized  in the computer vision community. However, to what extent the computer vision models are dependent on the context to make their predictions is unclear. A model overly relying on context will fail when encountering objects in different contexts than in training data and hence it is important to identify these dependencies before we can deploy the models in the real-world. We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models. We apply this methodology on two tasks, image classification and semantic segmentation, and discover undesirable dependency between objects and context, for example that "sidewalk" segmentation is very sensitive to the presence of "cars" in the image. We propose an object removal based data augmentation solution to mitigate this dependency and increase the robustness of classification and segmentation models to contextual variations. Our experiments show that the proposed data augmentation helps these models improve the performance in out-of-context scenarios, while preserving the performance on regular data.
Discovering Fair Representations in the Data Domain
Authors: Novi Quadrianto, Viktoriia Sharmanska, Oliver Thomas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness is by learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair outcomes. All available models however learn latent embeddings which comes at the cost of being uninterpretable. We propose to cast this problem as data-to-data translation, i.e. learning a mapping from an input domain to a fair target domain, where a fairness definition is being enforced. Here the data domain can be images, or any tabular data representation. This task would be straightforward if we had fair target data available, but this is not the case. To overcome this, we learn a highly unconstrained mapping by exploiting statistics of residuals -- the difference between input data and its translated version -- and the protected characteristics. When applied to the CelebA dataset of face images with gender attribute as the protected characteristic, our model enforces equality of opportunity by adjusting the eyes and lips regions. Intriguingly, on the same dataset we arrive at similar conclusions when using semantic attribute representations of images for translation. On face images of the recent DiF dataset, with the same gender attribute, our method adjusts nose regions. In the Adult income dataset, also with protected gender attribute, our model achieves equality of opportunity by, among others, obfuscating the wife and husband relationship. Analyzing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and prediction performance.
Actor-Critic Instance Segmentation
Authors: Nikita Araslanov, Constantin A. Rothkopf, Stefan Roth
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Most approaches to visual scene analysis have emphasised parallel processing of the image elements. However, one area in which the sequential nature of vision is apparent, is that of segmenting multiple, potentially similar and partially occluded objects in a scene. In this work, we revisit the recurrent formulation of this challenging problem in the context of reinforcement learning. Motivated by the limitations of the global max-matching assignment of the ground-truth segments to the recurrent states, we develop an actor-critic approach in which the actor recurrently predicts one instance mask at a time and utilises the gradient from a concurrently trained critic network. We formulate the state, action, and the reward such as to let the critic model long-term effects of the current prediction and in- corporate this information into the gradient signal. Furthermore, to enable effective exploration in the inherently high-dimensional action space of instance masks, we learn a compact representation using a conditional variational auto-encoder. We show that our actor-critic model consistently provides accuracy benefits over the recurrent baseline on standard instance segmentation benchmarks.
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Authors: Edgar Schonfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by generating either images or image features. However, the former misses fine-grained details and the latter requires learning a mapping associated with class embeddings. In this work, we take feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders. This leaves us with the required discriminative information about the image and classes in the latent features, on which we train a softmax classifier. The key to our approach is that we align the distributions learned from images and from side-information to construct latent features that contain the essential multi-modal information associated with unseen classes. We evaluate our learned latent features on several benchmark datasets, i.e. CUB, SUN, AWA1 and AWA2, and establish a new state of the art on generalized zero-shot as well as on few-shot learning. Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings.
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
Authors: Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, Zeynep Akata
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Semantic segmentation is one of the most fundamental problems in computer vision and pixel-level labelling in this context is particularly expensive. Hence, there have been several attempts to reduce the annotation effort such as learning from image level labels and bounding box annotations. In this paper we take this one step further and focus on the challenging task of zero- and few-shot learning of semantic segmentation. We define this task as image segmentation by assigning a label to every pixel even though either no labeled sample of that class was present during training, i.e. zero-label semantic segmentation, or only a few labeled samples were present, i.e. few-label semantic segmentation.Our goal is to transfer the knowledge from previously seen classes to novel classes. Our proposed semantic projection network (SPNet) achieves this goal by incorporating a class-level semantic information into any network designed for semantic segmentation, in an end-to-end manner. We also propose a benchmark for this task on the challenging COCO-Stuff and PASCAL VOC12 datasets. Our model is effective in segmenting novel classes, i.e. alleviating expensive dense annotations, but also in adapting to novel classes without forgetting its prior knowledge, i.e. generalized zero- and few-label semantic segmentation.
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation
Authors: Xinhong Ma, Tianzhu Zhang, Changsheng Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: To bridge source and target domains for domain adaptation, there are three important types of information including data structure, domain label, and class label. Most existing domain adaptation approaches exploit only one or two types of this information and cannot make them complement and enhance each other. Different from existing methods, we propose an end-to-end Graph Convolutional Adversarial Network (GCAN) for unsupervised domain adaptation by jointly modeling data structure, domain label, and class label in a unified deep framework. The proposed GCAN model enjoys several merits. First, to the best of our knowledge, this is the first work to model the three kinds of information jointly in a deep model for unsupervised domain adaptation. Second, the proposed model has designed three effective alignment mechanisms including structure-aware alignment, domain alignment, and class centroid alignment, which can learn domain-invariant and semantic representations effectively to reduce the domain discrepancy for domain adaptation. Extensive experimental results on five standard benchmarks demonstrate that the proposed GCAN algorithm performs favorably against state-of-the-art unsupervised domain adaptation methods.
Seamless Scene Segmentation
Authors: Lorenzo Porzi, Samuel Rota Bulo, Aleksander Colovic, Peter Kontschieder
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.
Unsupervised Image Matching and Object Discovery as Optimization
Authors: Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Perez, Jean Ponce
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Learning with complete or partial supervision is power- ful but relies on ever-growing human annotation efforts. As a way to mitigate this serious problem, as well as to serve specific applications, unsupervised learning has emerged as an important field of research. In computer vision, unsu- pervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object cate- gories among images in a collection, following the work of Cho et al. [12]. We show that the original approach can be reformulated and solved as a proper optimization problem. Experiments on several benchmarks establish the merit of our approach.
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
Authors: Qi Zhang, Antoni B. Chan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera,  too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd. Therefore, to solve the wide-area counting task requires multiple cameras with overlapping fields-of-view.  In this paper, we propose a deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene-level density map on the ground-plane of the 3D world. We consider 3 versions of the fusion framework: the late fusion model fuses camera-view density map; the naive early fusion model fuses camera-view feature maps; and the multi-view multi-scale early fusion model favors that features aligned to the same ground-plane point have consistent scales. We test our 3 fusion models on 3 multi-view counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view counting dataset containing a crowded street intersection. Our methods achieve state-of-the-art results compared to other multi-view counting baselines.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions
Authors: Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/aimagelab/show-control-and-tell.
Towards VQA Models That Can Read
Authors: Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, Marcus Rohrbach
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new "TextVQA" dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0.
Object-Aware Aggregation With Bidirectional Temporal Graph for Video Captioning
Authors: Junchao Zhang, Yuxin Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understand the global content of video, but also capture the detailed object information. Meanwhile, video representations have great impact on the quality of generated captions. Thus, it is important for video captioning to capture salient objects with their detailed temporal dynamics, and represent them using discriminative spatio-temporal representations.  In this paper, we propose a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object-aware local feature aggregation on detected object regions. The main novelties and advantages are:  (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object. (2) Object-aware aggregation: Learnable VLAD (Vector of Locally Aggregated Descriptors) models are constructed on object temporal trajectories and global frame sequence, which performs object-aware aggregation to learn discriminative representations. A hierarchical attention mechanism is also developed to distinguish different contributions of multiple objects. Experiments on two widely-used datasets demonstrate our OA-BTG achieves state-of-the-art performance in terms of BLEU@4, METEOR and CIDEr metrics.
Progressive Attention Memory Network for Movie Story Question Answering
Authors: Junyeong Kim, Minuk Ma, Kyungsu Kim, Sungjin Kim, Chang D. Yoo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper proposes the progressive attention memory network (PAMN) for movie story question answering (QA). Movie story QA is challenging compared to VQA in two aspects: (1) pinpointing the temporal parts relevant to answer the question is difficult as the movies are typically longer than an hour, (2) it has both video and subtitle where different questions require different modality to infer the answer. To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer. Experiments on publicly available benchmark datasets, MovieQA and TVQA, demonstrate that each feature contributes to our movie story QA architecture, PAMN, and improves performance to achieve the state-of-the-art result. Qualitative analysis by visualizing the inference mechanism of PAMN is also provided.
Memory-Attended Recurrent Network for Video Captioning
Authors: Wenjie Pei, Jiyuan Zhang, Xiangrong Wang, Lei Ke, Xiaoyong Shen, Yu-Wing Tai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is that it cannot capture the multiple visual context information of a word appearing in more than one relevant videos in training data. To tackle this limitation, we propose the Memory-Attended Recurrent Network (MARN) for video captioning, in which a memory structure is designed to explore the full-spectrum correspondence between a word and its various similar visual contexts across videos in training data. Thus, our model is able to achieve a more comprehensive understanding for each word and yield higher captioning quality. Furthermore, the built memory structure enables our method to model the compatibility between adjacent words explicitly instead of asking the model to learn implicitly, as most existing models do. Extensive validation on two real-word datasets demonstrates that our MARN consistently outperforms state-of-the-art methods.