mtybilly/Instruct2DS huggingface dataset:
Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces
Authors: Felix Kuhnke, Jorn Ostermann
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D face models. However, the differences (domain gap) between rendered (source-domain) and real-world (target-domain) images can cause low performance. Advances in visual domain adaptation allow reducing the influence of domain differences using adversarial neural networks, which match the feature spaces between domains by enforcing domain-invariant features. While previous work on visual domain adaptation generally assumes discrete and shared label spaces, these assumptions are both invalid for pose estimation tasks. We are the first to present domain adaptation for head pose estimation with a focus on partially shared and continuous label spaces. More precisely, we adapt the predominant weighting approaches to continuous label spaces by applying a weighted resampling of the source domain during training. To evaluate our approach, we revise and extend existing datasets resulting in a new benchmark for visual domain adaption. Our experiments show that our method improves the accuracy of head pose estimation for real-world images despite using only labels from synthetic images.
Flare in Interference-Based Hyperspectral Cameras
Authors: Eden Sassoon, Yoav Y. Schechner, Tali Treibitz
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Stray light (flare) is formed inside cameras by internal reflections between optical elements. We point out a flare effect of significant magnitude and implication to snapshot hyperspectral imagers. Recent technologies enable placing interference-based filters on individual pixels in imaging sensors. These filters have narrow transmission bands around custom wavelengths and high transmission efficiency. Cameras using arrays of such filters are compact, robust and fast. However, as opposed to traditional broad-band filters, which often absorb unwanted light, narrow band-pass interference filters reflect non-transmitted light. This is a source of very significant flare which biases hyperspectral measurements. The bias in any pixel depends on spectral content in other pixels. We present a theoretical image formation model for this effect, and quantify it through simulations and experiments. In addition, we test deflaring of signals affected by such flare.
Computational Hyperspectral Imaging Based on Dimension-Discriminative Low-Rank Tensor Recovery
Authors: Shipeng Zhang, Lizhi Wang, Ying Fu, Xiaoming Zhong, Hua Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Exploiting the prior information is fundamental for the image reconstruction in computational hyperspectral imaging. Existing methods usually unfold the 3D signal as a 1D vector and treat the prior information within different dimensions in an indiscriminative manner, which ignores the high-dimensionality nature of hyperspectral image (HSI) and thus results in poor quality reconstruction. In this paper, we propose to make full use of the high-dimensionality structure of the desired HSI to boost the reconstruction quality. We first build a high-order tensor by exploiting the nonlocal similarity in HSI. Then, we propose a dimension-discriminative low-rank tensor recovery (DLTR) model to characterize the structure prior adaptively in each dimension. By integrating the structure prior in DLTR with the system imaging process, we develop an optimization framework for HSI reconstruction, which is finally solved via the alternating minimization algorithm. Extensive experiments implemented with both synthetic and real data demonstrate that our method outperforms state-of-the-art methods.
Deep Optics for Monocular Depth Estimation and 3D Object Detection
Authors: Julie Chang, Gordon Wetzstein
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Depth estimation and 3D object detection are critical for scene understanding but remain challenging to perform with a single image due to the loss of 3D information during image capture. Recent models using deep neural networks have improved monocular depth estimation performance, but there is still difficulty in predicting absolute depth and generalizing outside a standard dataset. Here we introduce the paradigm of deep optics, i.e. end-to-end design of optics and image processing, to the monocular depth estimation problem, using coded defocus blur as an additional depth cue to be decoded by a neural network. We evaluate several optical coding strategies along with an end-to-end optimization scheme for depth estimation on three datasets, including NYU Depth v2 and KITTI. We find an optimized freeform lens design yields the best results, but chromatic aberration from a singlet lens offers significantly improved performance as well. We build a physical prototype and validate that chromatic aberrations improve depth estimation on real-world results. In addition, we train object detection networks on the KITTI dataset and show that the lens optimized for depth estimation also results in improved 3D object detection performance.
Physics-Based Rendering for Improving Robustness to Rain
Authors: Shirsendu Sukanta Halder, Jean-Francois Lalonde, Raoul de Charette
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: To improve the robustness to rain, we present a physically-based rain rendering pipeline for realistically inserting rain into clear weather images. Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog. We validate our rendering with a user study, proving our rain is judged 40% more realistic that state-of-the-art. Using our generated weather augmented Kitti and Cityscapes dataset, we conduct a thorough evaluation of deep object detection and semantic segmentation algorithms and show that their performance decreases in degraded weather, on the order of 15% for object detection and 60% for semantic segmentation. Furthermore, we show refining existing networks with our augmented images improves the robustness of both object detection and semantic segmentation algorithms. We experiment on nuScenes and measure an improvement of 15% for object detection and 35% for semantic segmentation compared to original rainy performance. Augmented databases and code are available on the project page.
ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
Authors: Bin Ding, Chengjiang Long, Ling Zhang, Chunxia Xiao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive steps. At each step a shadow attention detector is firstly exploited to generate an attention map which specifies shadow regions in the input image. Given the attention map, a negative residual by a shadow remover encoder will recover a shadow-lighter or even a shadow-free image. The discriminator is designed to classify whether the output image in the last progressive step is real or fake. Moreover, ARGAN is suitable to be trained with a semi-supervised strategy to make full use of sufficient unsupervised data. The experiments on four public datasets have demonstrated that our ARGAN is robust to detect both simple and complex shadows and to produce more realistic shadow removal results. It outperforms the state-of-the-art methods, especially in detail of recovering shadow areas.
Deep Tensor ADMM-Net for Snapshot Compressive Imaging
Authors: Jiawei Ma, Xiao-Yang Liu, Zheng Shou, Xin Yuan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Snapshot compressive imaging (SCI) systems have been developed to capture high-dimensional (> 3) signals using low-dimensional off-the-shelf sensors, i.e., mapping multiple video frames into a single measurement frame. One key module of a SCI system is an accurate decoder that recovers the original video frames. However, existing model-based decoding algorithms require exhaustive parameter tuning with prior knowledge and cannot support practical applications due to the extremely long running time. In this paper, we propose a deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds. Firstly, we start with a standard tensor ADMM algorithm, unfold its inference iterations into a layer-wise structure, and design a deep neural network based on tensor operations. Secondly, instead of relying on a pre-specified sparse representation domain, the network learns the domain of low-rank tensor through stochastic gradient descent. It is worth noting that the proposed deep tensor ADMM-Net has potentially mathematical interpretations. On public video data, the simulation results show the proposed  method  achieves average 0.8 ~ 2.5 dB improvement in PSNR and 0.07 ~ 0.1 in SSIM, and 1500x~ 3600 xspeedups over the state-of-the-art methods. On real data captured by SCI cameras, the experimental results show comparable visual results with the state-of-the-art methods but in much shorter running time.
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision
Authors: Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper, we formulate a generic non-minimal solver using the existing tools of Polynomials Optimization Problems (POP) from computational algebraic geometry. The proposed method exploits the well known Shor's or Lasserre's relaxations, whose theoretical aspects are also discussed. Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision. Our framework is simple and straightforward to implement, which is also supported by three diverse applications in 3D vision, namely rigid body transformation estimation, Non-Rigid Structure-from-Motion (NRSfM), and camera autocalibration. In all three cases, both non-minimal and consensus maximization are tested, which are also compared against the state-of-the-art methods. Our results are competitive to the compared methods, and are also coherent with our theoretical analysis. The main contribution of this paper is the claim that a good approximate solution for many polynomial problems involved in 3D vision can be obtained using the existing theory of numerical computational algebra. This claim leads us to reason about why many relaxed methods in 3D vision behave so well? And also allows us to offer a generic relaxed solver in a rather straightforward way. We further show that the convex relaxation of these polynomials can easily be used for maximizing consensus in a deterministic manner. We support our claim using several experiments for aforementioned three diverse problems in 3D vision.
Pareto Meets Huber: Efficiently Avoiding Poor Minima in Robust Estimation
Authors: Christopher Zach, Guillaume Bourmaud
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Robust cost optimization is the task of fitting parameters to data points containing outliers. In particular, we focus on large-scale computer vision problems, such as bundle adjustment, where Non-Linear Least Square (NLLS) solvers are the current workhorse. In this context, NLLS-based state of the art algorithms have been designed either to quickly improve the target objective and find a local minimum close to the initial value of the parameters, or to have a strong ability to escape poor local minima. In this paper, we propose a novel algorithm relying on multi-objective optimization which allows to match those two properties. We experimentally demonstrate that our algorithm has an ability to escape poor local minima that is on par with the best performing algorithms with a faster decrease of the target objective.
K-Best Transformation Synchronization
Authors: Yifan Sun, Jiacheng Zhuo, Arnav Mohan, Qixing Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper, we introduce the problem of K-best transformation synchronization for the purpose of multiple scan matching. Given noisy pair-wise transformations computed between a subset of depth scan pairs, K-best transformation synchronization seeks to output multiple consistent relative transformations. This problem naturally arises in many geometry reconstruction applications, where the underlying object possesses self-symmetry. For approximately symmetric or even non-symmetric objects, K-best solutions offer an intermediate presentation for recovering the underlying single-best solution. We introduce a simple yet robust iterative algorithm for K-best transformation synchronization, which alternates between transformation propagation and transformation clustering. We present theoretical guarantees on the robust and exact recoveries of our algorithm. Experimental results demonstrate the advantage of our approach against state-of-the-art transformation synchronization techniques on both synthetic and real datasets.
Parametric Majorization for Data-Driven Energy Minimization Methods
Authors: Jonas Geiping, Michael Moeller
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Energy minimization methods are a classical tool in a multitude of computer vision applications. While they are interpretable and well-studied, their regularity assumptions are difficult to design by hand. Deep learning techniques on the other hand are purely data-driven, often provide excellent results, but are very difficult to constrain to predefined physical or safety-critical models. A possible combination between the two approaches is to design a parametric energy and train the free parameters in such a way that minimizers of the energy correspond to desired solution on a set of training examples. Unfortunately, such formulations typically lead to bi-level optimization problems, on which common optimization algorithms are difficult to scale to modern requirements in data processing and efficiency. In this work, we present a new strategy to optimize these bi-level problems. We investigate surrogate single-level problems that majorize the target problems and can be implemented with existing tools, leading to efficient algorithms without collapse of the energy function. This framework of strategies enables new avenues to the training of parameterized energy minimization models from large data.
A Bayesian Optimization Framework for Neural Network Compression
Authors: Xingchen Ma, Amal Rannen Triki, Maxim Berman, Christos Sagonas, Jacques Cali, Matthew B. Blaschko
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Neural network compression is an important step for deploying neural networks where speed is of high importance, or on devices with limited memory. It is necessary to tune compression parameters in order to achieve the desired trade-off between size and performance. This is often done by optimizing the loss on a validation set of data, which should be large enough to approximate the true risk and therefore yield sufficient generalization ability. However, using a full validation set can be computationally expensive. In this work, we develop a general Bayesian optimization framework for optimizing functions that are computed based on U-statistics. We propagate Gaussian uncertainties from the statistics through the Bayesian optimization framework yielding a method that gives a probabilistic approximation certificate of the result. We then apply this to parameter selection in neural network compression. Compression objectives that can be written as U-statistics are typically based on empirical risk and knowledge distillation for deep discriminative models. We demonstrate our method on VGG and ResNet models, and the resulting system can find optimal compression parameters for relatively high-dimensional parametrizations in a matter of minutes on a standard desktop machine, orders of magnitude faster than competing methods.
HiPPI: Higher-Order Projected Power Iterations for Scalable Multi-Matching
Authors: Florian Bernard, Johan Thunberg, Paul Swoboda, Christian Theobalt
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple (NP-hard) quadratic assignment problems (QAPs) that are coupled via cycle-consistency constraints. The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their (relatively) high computational cost. We address these shortcomings by introducing a Higher-order Projected Power Iteration method, which is (i) efficient and scales to tens of thousands of points, (ii) straightforward to implement, (iii) able to incorporate geometric consistency, (iv) guarantees cycle-consistent multi-matchings, and (iv) comes with theoretical convergence guarantees. Experimentally we show that our approach is superior to existing methods.
Language-Conditioned Graph Networks for Relational Reasoning
Authors: Ronghang Hu, Anna Rohrbach, Trevor Darrell, Kate Saenko
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Solving grounded language tasks often requires reasoning about relationships between objects in the context of a given task. For example, to answer the question "What color is the mug on the plate?" we must check the color of the specific mug that satisfies the "on" relationship with respect to the plate. Recent work has proposed various methods capable of complex relational reasoning. However, most of their power is in the inference structure, while the scene is represented with simple local appearance features. In this paper, we take an alternate approach and build contextualized representations for objects in a visual scene to support relational reasoning. We propose a general framework of Language-Conditioned Graph Networks (LCGN), where each node represents an object, and is described by a context-aware representation from related objects through iterative message passing conditioned on the textual input. E.g., conditioning on the "on" relationship to the plate, the object "mug" gathers messages from the object "plate" to update its representation to "mug on the plate", which can be easily consumed by a simple classifier for answer prediction. We experimentally show that our LCGN approach effectively supports relational reasoning and improves performance across several tasks and datasets. Our code is available at http://ronghanghu.com/lcgn.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Authors: Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/.
Relation-Aware Graph Attention Network for Visual Question Answering
Authors: Linjie Li, Zhe Gan, Yu Cheng, Jingjing Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA.
Unpaired Image Captioning via Scene Graph Alignments
Authors: Jiuxiang Gu, Shafiq Joty, Jianfei Cai, Handong Zhao, Xu Yang, Gang Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.
Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning
Authors: Yannick Le Cacheux, Herve Le Borgne, Michel Crucianu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recognizing visual unseen classes, i.e. for which no training data is available, is known as Zero Shot Learning (ZSL). Some of the best performing methods apply the triplet loss to seen classes to learn a mapping between visual representations of images and attribute vectors that constitute class prototypes. They nevertheless make several implicit assumptions that limit their performance on real use cases, particularly with fine-grained datasets comprising a large number of classes. We identify three of these assumptions and put forward corresponding novel contributions to address them. Our approach consists in taking into account both inter-class and intra-class relations, respectively by being more permissive with confusions between similar classes, and by penalizing visual samples which are atypical to their class. The approach is tested on four datasets, including the large-scale ImageNet, and exhibits performances significantly above recent methods, even generative methods based on more restrictive hypotheses.
Occlusion-Shared and Feature-Separated Network for Occlusion Relationship Reasoning
Authors: Rui Lu, Feng Xue, Menghan Zhou, Anlong Ming, Yu Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Occlusion relationship reasoning demands closed contour to express the object, and orientation of each contour pixel to describe the order relationship between objects. Current CNN-based methods neglect two critical issues of the task: (1) simultaneous existence of the relevance and distinction for the two elements, i.e, occlusion edge and occlusion orientation; and (2) inadequate exploration to the orientation features. For the reasons above, we propose the Occlusion-shared and Feature-separated Network (OFNet). On one hand, considering the relevance between edge and orientation, two sub-networks are designed to share the occlusion cue. On the other hand, the whole network is split into two paths to learn the high semantic features separately. Moreover, a contextual feature for orientation prediction is extracted, which represents the bilateral cue of the foreground and background areas. The bilateral cue is then fused with the occlusion cue to precisely locate the object regions. Finally, a stripe convolution is designed to further aggregate features from surrounding scenes of the occlusion edge. The proposed OFNet remarkably advances the state-of-the-art approaches on PIOD and BSDS ownership dataset.
Mixture-Kernel Graph Attention Network for Situation Recognition
Authors: Mohammed Suhail, Leonid Sigal
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Understanding images beyond salient actions involves reasoning about scene context, objects, and the roles they play in the captured event. Situation recognition has recently been introduced as the task of jointly reasoning about the verbs (actions) and a set of semantic-role and entity (noun) pairs in the form of action frames. Labeling an image with an action frame requires an assignment of values (nouns) to the roles based on the observed image content. Among the inherent challenges are the rich conditional structured dependencies between the output role assignments and the overall semantic sparsity. In this paper, we propose a novel mixture-kernel attention graph neural network (GNN) architecture designed to address these challenges. Our GNN enables dynamic graph structure during training and inference, through the use of a graph attention mechanism, and context-aware interactions between role pairs. We illustrate the efficacy of our model and design choices by conducting experiments on imSitu benchmark dataset, with accuracy improvements of up to 10% over the state-of-the-art.
Learning Similarity Conditions Without Explicit Supervision
Authors: Reuben Tan, Mariya I. Vasileva, Kate Saenko, Bryan A. Plummer
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Many real-world tasks require models to compare images along multiple similarity conditions (e.g. similarity in color, category or shape). Existing methods often reason about these complex similarity relationships by learning condition-aware embeddings. While such embeddings aid models in learning different notions of similarity, they also limit their capability to generalize to unseen categories since they require explicit labels at test time. To address this deficiency, we propose an approach that jointly learns representations for the different similarity conditions and their contributions as a latent variable without explicit supervision. Comprehensive experiments across three datasets, Polyvore-Outfits, Maryland-Polyvore and UT-Zappos50k, demonstrate the effectiveness of our approach: our model outperforms the state-of-the-art methods, even those that are strongly supervised with pre-defined similarity conditions, on fill-in-the-blank, outfit compatibility prediction and triplet prediction tasks. Finally, we show that our model learns different visually-relevant semantic sub-spaces that allow it to generalize well to unseen categories.
Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes
Authors: Huikun Bi, Zhong Fang, Tianlu Mao, Zhaoqi Wang, Zhigang Deng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Trajectory prediction for objects is challenging and critical for various applications (e.g., autonomous driving, and anomaly detection). Most of the existing methods focus on homogeneous pedestrian trajectories prediction, where pedestrians are treated as particles without size. However, they fall short of handling crowded vehicle-pedestrian-mixed scenes directly since vehicles, limited with kinematics in reality, should be treated as rigid, non-particle objects ideally. In this paper, we tackle this problem using separate LSTMs for heterogeneous vehicles and pedestrians. Specifically, we use an oriented bounding box to represent each vehicle, calculated based on its position and orientation, to denote its kinematic trajectories. We then propose a framework called VP-LSTM to predict the kinematic trajectories of both vehicles and pedestrians simultaneously. In order to evaluate our model, a large dataset containing the trajectories of both vehicles and pedestrians in vehicle-pedestrian-mixed scenes is specially built. Through comparisons between our method with state-of-the-art approaches, we show the effectiveness and advantages of our method on kinematic trajectories prediction in vehicle-pedestrian-mixed scenes.
Learning to Caption Images Through a Lifetime by Asking Questions
Authors: Tingke Shen, Amlan Kar, Sanja Fidler
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In order to bring artificial agents into our lives, we will need to go beyond supervised learning on closed datasets to having the ability to continuously expand knowledge. Inspired by a student learning in a classroom, we present an agent that can continuously learn by posing natural language questions to humans. Our agent is composed of three interacting modules, one that performs captioning, another that generates questions and a decision maker that learns when to ask questions by implicitly reasoning about the uncertainty of the agent and expertise of the teacher. As compared to current active learning methods which query images for full captions, our agent is able to ask pointed questions to improve the generated captions. The agent trains on the improved captions, expanding its knowledge. We show that our approach achieves better performance using less human supervision than the baselines on the challenging MSCOCO dataset.
VrR-VG: Refocusing Visually-Relevant Relationships
Authors: Yuanzhi Liang, Yalong Bai, Wei Zhang, Xueming Qian, Li Zhu, Tao Mei
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Relationships encode the interactions among individual instances and play a critical role in deep visual scene understanding. Suffering from the high predictability with non-visual information, relationship models tend to fit the statistical bias rather than "learning" to infer the relationships from images. To encourage further development in visual relationships, we propose a novel method to mine more valuable relationships by automatically pruning visually-irrelevant relationships. We construct a new scene graph dataset named Visually-Relevant Relationships Dataset (VrR-VG) based on Visual Genome. Compared with existing datasets, the performance gap between learnable and statistical method is more significant in VrR-VG, and frequency-based analysis does not work anymore. Moreover, we propose to learn a relationship-aware representation by jointly considering instances, attributes and relationships. By applying the representation-aware feature learned on VrR-VG, the performances of image captioning and visual question answering are systematically improved, which demonstrates the effectiveness of both our dataset and features embedding schema. Both our VrR-VG dataset and representation-aware features will be made publicly available soon.
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo
Authors: Andrea Romanoni, Matteo Matteucci
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3D points cloud. This approach relies on photo-consistency to evaluate the goodness of a depth estimate. It generally produces very accurate results; however, the reconstructed model often lacks completeness, especially in correspondence of broad untextured areas where the photo-consistency metrics are unreliable. Assuming the untextured areas piecewise planar, in this paper we generate novel PatchMatch hypotheses so to expand reliable depth estimates in neighboring untextured regions. At the same time, we modify the photo-consistency measure such to favor standard or novel PatchMatch depth hypotheses depending on the textureness of the considered area. We also propose a depth refinement step to filter wrong estimates and to fill the gaps on both the depth maps and normal maps while preserving the discontinuities. The effectiveness of our new methods has been tested against several state of the art algorithms in the publicly available ETH3D dataset containing a wide variety of high and low-resolution images.
U4D: Unsupervised 4D Dynamic Scene Understanding
Authors: Armin Mustafa, Chris Russell, Adrian Hilton
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video. Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction, together with instance-level segmentation exploiting photo-consistency, semantic and motion information. We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction. This enables per person semantic instance segmentation of multiple interacting people in complex dynamic scenes. Extensive evaluation of the joint visual scene understanding framework against state-of-the-art methods on challenging indoor and outdoor sequences demonstrates a significant (approx 40%) improvement in semantic segmentation, reconstruction and scene flow accuracy.
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
Authors: Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge branch to hierarchically integrate point features and generate edge features. To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process. For each edge in the final graph, we predict a label to indicate the semantic consistency of the two connected points to enhance point prediction. At different layers, edge features are also fed into the corresponding point module to integrate contextual information for message passing enhancement in local regions. The two branches interact with each other and cooperate in segmentation. Decent experimental results on several 3D semantic labeling datasets demonstrate the effectiveness of our work.
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds From Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Authors: Zhizhong Han, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from self-reconstruction. However, these methods are still suffering from ineffective learning of local geometry, which significantly limits the discriminability of learned features. To resolve this issue, we propose MAP-VAE to enable the learning of global and local geometry by jointly leveraging global and local self-supervision. To enable effective local self-supervision, we introduce multi-angle analysis for point clouds. In a multi-angle scenario, we first split a point cloud into a front half and a back half from each angle, and then, train MAP-VAE to learn to predict a back half sequence from the corresponding front half sequence. MAP-VAE performs this half-to-half prediction using RNN to simultaneously learn each local geometry and the spatial relationship among them. In addition, MAP-VAE also learns global geometry via self-reconstruction, where we employ a variational constraint to facilitate novel shape generation. The outperforming results in four shape analysis tasks show that MAP-VAE can learn more discriminative global or local features than the state-of-the-art methods.
P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo
Authors: Keyang Luo, Tao Guan, Lili Ju, Haipeng Huang, Yawei Luo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Learning-based methods are demonstrating their strong competitiveness in estimating depth for multi-view stereo reconstruction in recent years. Among them the approaches that generate cost volumes based on the plane-sweeping algorithm and then use them for feature matching have shown to be very prominent recently. The plane-sweep volumes are essentially anisotropic in depth and spatial directions, but they are often approximated by isotropic cost volumes in those methods, which could be detrimental. In this paper, we propose a new end-to-end deep learning network of P-MVSNet for multi-view stereo based on isotropic and anisotropic 3D convolutions. Our P-MVSNet consists of two core modules: a patch-wise aggregation module learns to aggregate the pixel-wise correspondence information of extracted features to generate a matching confidence volume, from which a hybrid 3D U-Net then infers a depth probability distribution and predicts the depth maps. We perform extensive experiments on the DTU and Tanks & Temples benchmark datasets, and the results show that the proposed P-MVSNet achieves the state-of-the-art performance over many existing methods on multi-view stereo.
SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement Learning
Authors: Yung-Han Ho, Chuan-Yuan Cho, Wen-Hsiao Peng, Guo-Lun Jin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper leverages a classic prediction technique, known as parametric overlapped block motion compensation (POBMC), in a reinforcement learning framework for video prediction. Learning-based prediction methods with explicit motion models often suffer from having to estimate large numbers of motion parameters with artificial regularization. Inspired by the success of sparse motion-based prediction for video compression, we propose a parametric video prediction on a sparse motion field composed of few critical pixels and their motion vectors. The prediction is achieved by gradually refining the estimate of a future frame in iterative, discrete steps. Along the way, the identification of critical pixels and their motion estimation are addressed by two neural networks trained under a reinforcement learning setting. Our model achieves the state-of-the-art performance on CaltchPed, UCF101 and CIF datasets in one-step and multi-step prediction tests. It shows good generalization results and is able to learn well on small training data.
ClothFlow: A Flow-Based Model for Clothed Person Generation
Authors: Xintong Han, Xiaojun Hu, Weilin Huang, Matthew R. Scott
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present ClothFlow, an appearance-flow-based generative model to synthesize clothed person for posed-guided person image generation and virtual try-on. By estimating a dense flow between source and target clothing regions, ClothFlow effectively models the geometric changes and naturally transfers the appearance to synthesize novel images as shown in Figure 1. We achieve this with a three-stage framework: 1) Conditioned on a target pose, we first estimate a person semantic layout to provide richer guidance to the generation process. 2) Built on two feature pyramid networks, a cascaded flow estimation network then accurately estimates the appearance matching between corresponding clothing regions. The resulting dense flow warps the source image to flexibly account for deformations. 3) Finally, a generative network takes the warped clothing regions as inputs and renders the target view. We conduct extensive experiments on the DeepFashion dataset for pose-guided person image generation and on the VITON dataset for the virtual try-on task. Strong qualitative and quantitative results validate the effectiveness of our method.
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
Authors: Qiao Gu, Guanzhi Wang, Mang Tik Chiu, Yu-Wing Tai, Chi-Keung Tang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network for achieving local detail transfer between facial images, with the use of asymmetric loss functions for dramatic makeup styles with high-frequency details. Existing techniques do not demonstrate or fail to transfer high-frequency details in a global adversarial setting, or train a single local discriminator only to ensure image structure consistency and thus work only for relatively simple styles. Unlike others, our proposed local adversarial discriminators can distinguish whether the generated local image details are consistent with the corresponding regions in the given reference image in cross-image style transfer in an unsupervised setting. Incorporating these technical contributions, we achieve not only state-of-the-art results on conventional styles but also novel results involving complex and dramatic styles with high-frequency details covering large areas across multiple facial features. A carefully designed dataset of unpaired before and after makeup images is released at https://georgegu1997.github.io/LADN-project-page.
Point-to-Point Video Generation
Authors: Tsun-Hsuan Wang, Yen-Chi Cheng, Chieh Hubert Lin, Hwann-Tzong Chen, Min Sun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: While image synthesis achieves tremendous breakthroughs (e.g., generating realistic faces), video generation is less explored and harder to control, which limits its applications in the real world. For instance, video editing requires temporal coherence across multiple clips and thus poses both start and end constraints within a video sequence. We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames. The task is challenging since the model not only generates a smooth transition of frames but also plans ahead to ensure that the generated end-frame conforms to the targeted end-frame for videos of various lengths. We propose to maximize the modified variational lower bound of conditional data likelihood under a skip-frame training strategy. Our model can generate end-frame-consistent sequences without loss of quality and diversity. We evaluate our method through extensive experiments on Stochastic Moving MNIST, Weizmann Action, Human3.6M, and BAIR Robot Pushing under a series of scenarios. The qualitative results showcase the effectiveness and merits of point-to-point generation.
Semantics-Enhanced Adversarial Nets for Text-to-Image Synthesis
Authors: Hongchen Tan, Xiuping Liu, Xin Li, Yi Zhang, Baocai Yin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper presents a new model, Semantics-enhanced Generative Adversarial Network (SEGAN), for fine-grained text-to-image generation. We introduce two modules, a Semantic Consistency Module (SCM) and an Attention Competition Module (ACM), to our SEGAN. The SCM incorporates image-level semantic consistency into the training of the Generative Adversarial Network (GAN), and can diversify the generated images and improve their structural coherence. A Siamese network and two types of semantic similarities are designed to map the synthesized image and the groundtruth image to nearby points in the latent semantic feature space. The ACM constructs adaptive attention weights to differentiate keywords from unimportant words, and improves the stability and accuracy of SEGAN. Extensive experiments demonstrate that our SEGAN significantly outperforms existing state-of-the-art methods in generating photo-realistic images. All source codes and models will be released for comparative study.