mtybilly/Instruct2DS huggingface dataset:
A Multi-Hypothesis Approach to Color Constancy
Authors: Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam, Ales Leonardis, Gregory Slabaugh, Steven McDonagh
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce point estimates that do not explicitly account for potential ambiguities among plausible illuminant solutions, due to the ill-posed nature of the problem. We propose a Bayesian framework that naturally handles color constancy ambiguity via a multi-hypothesis strategy. Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate a set of corrected images. Secondly, we estimate, for each corrected image, the likelihood of the light source being achromatic using a camera-agnostic CNN. Finally, our method explicitly learns a final illumination estimate from the generated posterior probability distribution. Our likelihood estimator learns to answer a camera-agnostic question and thus enables effective multi-camera training by disentangling illuminant estimation from the supervised learning task. We extensively evaluate our proposed approach and additionally set a benchmark for novel sensor generalisation without re-training. Our method provides state-of-the-art accuracy on multiple public datasets (up to 11% median angular error improvement) while maintaining real-time execution.
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
Authors: John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present MSeg, a composite dataset that unifies se- mantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
Learning Event-Based Motion Deblurring
Authors: Zhe Jiang, Yu Zhang, Dongqing Zou, Jimmy Ren, Jiancheng Lv, Yebin Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process. For event-based cameras, however, fast motion can be captured as events at high frame rate, raising new opportunities to exploring effective solutions. In this paper, we start from a sequential formulation of event-based motion deblurring, then show how its optimization can be unfolded with a novel end-toend deep architecture. The proposed architecture is a convolutional recurrent neural network that integrates visual and temporal knowledge of both global and local scales in principled manner. To further improve the reconstruction, we propose a differentiable directional event filtering module to effectively extract rich boundary prior from the evolution of events. We conduct extensive experiments on the synthetic GoPro dataset and a large newly introduced dataset captured by a DAVIS240C camera. The proposed approach achieves state-of-the-art reconstruction quality, and generalizes better to handling real-world motion blur.
DMCP: Differentiable Markov Channel Pruning for Neural Networks
Authors: Shaopeng Guo, Yujie Wang, Quanquan Li, Junjie Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recent works imply that the channel pruning can be regarded as searching optimal sub-structure from unpruned networks. However, existing works based on this observation require training and evaluating a large number of structures, which limits their application. In this paper, we propose a novel differentiable method for channel pruning, named Differentiable Markov Channel Pruning (DMCP), to efficiently search the optimal sub-structure. Our method is differentiable and can be directly optimized by gradient descent with respect to standard task loss and budget regularization (e.g. FLOPs constraint). In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process. In the end, our method is able to implicitly select the proper number of channels in each layer by the Markov process with optimized transitions. To validate the effectiveness of our method, we perform extensive experiments on Imagenet with ResNet and MobilenetV2. Results show our method can achieve consistent improvement than state-of-the-art pruning methods in various FLOPs settings.
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement
Authors: Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, Jiaying Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement. A deep recursive band network (DRBN) is proposed to recover a linear band representation of an enhanced normal-light image with paired low/normal-light images, and then obtain an improved one by recomposing the given bands via another learnable linear transformation based on a perceptual quality-driven adversarial learning with unpaired data. The architecture is powerful and flexible to have the merit of training with both paired and unpaired data. On one hand, the proposed network is well designed to extract a series of coarse-to-fine band representations, whose estimations are mutually beneficial in a recursive process. On the other hand, the extracted band representation of the enhanced image in the first stage of DRBN (recursive band learning) bridges the gap between the restoration knowledge of paired data and the perceptual quality preference to real high-quality images. Its second stage (band recomposition) learns to recompose the band representation towards fitting perceptual properties of high-quality images via adversarial learning. With the help of this two-stage design, our approach generates enhanced results with well-reconstructed details and visually promising contrast and color distributions. Qualitative and quantitative evaluations demonstrate the superiority of our DRBN.
Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation
Authors: Junsong Fan, Zhaoxiang Zhang, Chunfeng Song, Tieniu Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Image-level weakly-supervised semantic segmentation (WSSS) aims at learning semantic segmentation by adopting only image class labels. Existing approaches generally rely on class activation maps (CAM) to generate pseudo-masks and then train segmentation models. The main difficulty is that the CAM estimate only covers partial foreground objects. In this paper, we argue that the critical factor preventing to obtain the full object mask is the classification boundary mismatch problem in applying the CAM to WSSS. Because the CAM is optimized by the classification task, it focuses on the discrimination across different image-level classes. However, the WSSS requires to distinguish pixels sharing the same image-level class to separate them into the foreground and the background. To alleviate this contradiction, we propose an efficient end-to-end Intra-Class Discriminator (ICD) framework, which learns intra-class boundaries to help separate the foreground and the background within each image-level class. Without bells and whistles, our approach achieves the state-of-the-art performance of image label based WSSS, with mIoU 68.0% on the VOC 2012 semantic segmentation benchmark, demonstrating the effectiveness of the proposed approach.
Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction
Authors: Yantao Lu, Yunhan Jia, Jianyu Wang, Bai Li, Weiheng Chai, Lawrence Carin, Senem Velipasalar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other models. Although significant effort has been devoted to the transferability across models, surprisingly little attention has been paid to cross-task transferability, which represents the real-world cybercriminal's situation, where an ensemble of different defense/detection mechanisms need to be evaded all at once. We investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, object detection, semantic segmentation, explicit content detection, and text detection. Our proposed attack minimizes the "dispersion" of the internal feature map, overcoming the limitations of existing attacks, that require task-specific loss functions and/or probing a target model. We conduct evaluation on open-source detection and segmentation models, as well as four different computer vision tasks provided by Google Cloud Vision (GCV) APIs. We demonstrate that our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations.
Mesh-Guided Multi-View Stereo With Pyramid Architecture
Authors: Yuesong Wang, Tao Guan, Zhuo Chen, Yawei Luo, Keyang Luo, Lili Ju
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Multi-view stereo (MVS) aims to reconstruct 3D geometry of the target scene by using only information from 2D images. Although much progress has been made, it still suffers from textureless regions. To overcome this difficulty, we propose a mesh-guided MVS method with pyramid architecture, which makes use of the surface mesh obtained from coarse-scale images to guide the reconstruction process. Specifically, a PatchMatch-based MVS algorithm is first used to generate depth maps for coarse-scale images and the corresponding surface mesh is obtained by a surface reconstruction algorithm. Next we project the mesh onto each of depth maps to replace unreliable depth values and the corrected depth maps are fed to fine-scale reconstruction for initialization. To alleviate the influence of possible erroneous faces on the mesh, we further design and train a convolutional neural network to remove incorrect depths. In addition, it is often hard for the correct depth values for low-textured regions to survive at the fine-scale, thus we also develop an efficient method to seek out these regions and further enforce the geometric consistency in these regions. Experimental results on the ETH3D high-resolution dataset demonstrate that our method achieves state-of-the-art performance, especially in completeness.
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
Authors: Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a minor role in the deep learning revolution. Leading methods for learning generative models of shapes rely on implicit functions, and generate meshes only after expensive iso-surfacing routines. To overcome these challenges, we are inspired by a classical spatial data structure from computer graphics, Binary Space Partitioning (BSP), to facilitate 3D learning. The core ingredient of BSP is an operation for recursive subdivision of space to obtain convex sets. By exploiting this property, we devise BSP-Net, a network that learns to represent a 3D shape via convex decomposition. Importantly, BSP-Net is unsupervised since no convex shape decompositions are needed for training. The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes. The convexes inferred by BSP-Net can be easily extracted to form a polygon mesh, without any need for iso-surfacing. The generated meshes are compact (i.e., low-poly) and well suited to represent sharp geometry; they are guaranteed to be watertight and can be easily parameterized. We also show that the reconstruction quality by BSP-Net is competitive with state-of-the-art methods while using much fewer primitives. Code is available at https://github.com/czq142857/BSP-NET-original.
Which Is Plagiarism: Fashion Image Retrieval Based on Regional Representation for Design Protection
Authors: Yining Lang, Yuan He, Fan Yang, Jianfeng Dong, Hui Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: With the rapid growth of e-commerce and the popularity of online shopping, fashion retrieval has received considerable attention in the computer vision community. Different from the existing works that mainly focus on identical or similar fashion item retrieval, in this paper, we aim to study the plagiarized clothes retrieval which is somewhat ignored in the academic community while itself has great application value. One of the key challenges is that plagiarized clothes are usually modified in a certain region on the original design to escape the supervision by traditional retrieval methods. To relieve it, we propose a novel network named Plagiarized-Search-Net (PS-Net) based on regional representation, where we utilize the landmarks to guide the learning of regional representations and compare fashion items region by region. Besides, we propose a new dataset named Plagiarized Fashion for plagiarized clothes retrieval, which provides a meaningful complement to the existing fashion retrieval field. Experiments on Plagiarized Fashion dataset verify that our approach is superior to other instance-level counterparts for plagiarized clothes retrieval, showing a promising result for original design protection. Moreover, our PS-Net can also be adapted to traditional fashion retrieval and landmark estimation tasks and achieves the state-of-the-art performance on the DeepFashion and DeepFashion2 datasets.
Relation-Aware Global Attention for Person Re-Identification
Authors: Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Xin Jin, Zhibo Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matches well the key of re-id, i.e., discriminative feature learning. Previous approaches typically learn attention using local convolutions, ignoring the mining of knowledge from global structure patterns. Intuitively, the affinities among spatial positions/nodes in the feature map provide clustering-like information and are helpful for inferring semantics and thus attention, especially for person images where the feasible human poses are constrained. In this work, we propose an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning. Specifically, for each feature position, in order to compactly grasp the structural information of global scope and local appearance information, we propose to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions (e.g., in raster scan order), and the feature itself together to learn the attention with a shallow convolutional model. Extensive ablation studies demonstrate that our RGA can significantly enhance the feature representation power and help achieve the state-of-the-art performance on several popular benchmarks. The source code is available at https://github.com/microsoft/Relation-Aware-Global-Attention-Networks.
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
Authors: Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A. K. Qin, Yun Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and imperceptible perturbations, or physical-world adversarial examples created with large and less realistic distortions that are easily identified by human observers. In this paper, we propose a novel approach, called Adversarial Camouflage (AdvCam), to craft and camouflage physical-world adversarial examples into natural styles that appear legitimate to human observers. Specifically, AdvCam transfers large adversarial perturbations into customized styles, which are then "hidden" on-target object or off-target background. Experimental evaluation shows that, in both digital and physical-world scenarios, adversarial examples crafted by AdvCam are well camouflaged and highly stealthy, while remaining effective in fooling state-of-the-art DNN image classifiers. Hence, AdvCam is a flexible approach that can help craft stealthy attacks to evaluate the robustness of DNNs.
Evolving Losses for Unsupervised Video Representation Learning
Authors: AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present a new method to learn video representations from large-scale unlabeled video data. Ideally, this representation will be generic and transferable, directly usable for new tasks such as action recognition and zero or few-shot learning. We formulate unsupervised representation learning as a multi-modal, multi-task learning problem, where the representations are shared across different modalities via distillation. Further, we introduce the concept of loss function evolution by using an evolutionary search algorithm to automatically find optimal combination of loss functions capturing many (self-supervised) tasks and modalities. Thirdly, we propose an unsupervised representation evaluation metric using distribution matching to a large unlabeled dataset as a prior constraint, based on Zipf's law. This unsupervised constraint, which is not guided by any labeling, produces similar results to weakly-supervised, task-specific ones. The proposed unsupervised representation learning results in a single RGB network and outperforms previous methods. Notably, it is also more effective than several label-based methods (e.g., ImageNet), with the exception of large, fully labeled video datasets.
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
Authors: Yancheng Wang, Yang Xiao, Fu Xiong, Wenxiang Jiang, Zhiguo Cao, Joey Tianyi Zhou, Junsong Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: For depth-based 3D action recognition, one essential issue is to represent 3D motion pattern effectively and efficiently. To this end, 3D dynamic voxel (3DV) is proposed as a novel 3D motion representation manner. With 3D space voxelization, the key idea of 3DV is to encode the 3D motion information within depth video into a regular voxel set (i.e., 3DV) compactly, via temporal rank pooling. Each available 3DV voxel intrinsically involves 3D spatial and motion feature for 3D action description. 3DV is then abstracted as a point set and input into PointNet++ for 3D action recognition, in the end-to-end learning way. The intuition for transferring 3DV into the point set form is that, PointNet++ is lightweight and effective for deep feature learning towards point set. Since 3DV may loose appearance clue, a multi-stream 3D action recognition manner is also proposed to learn motion and appearance feature jointly. To extract richer temporal order information of actions, we also split the depth video into temporal segments and encode this procedure in 3DV integrally. The extensive experiments on the well-established benchmark datasets (e.g., NTU RGB+D 120 and NTU RGB+D 60) demonstrate the superiority of our proposition. Impressively, we acquire the accuracy of 82.4% and 93.5% on NTU RGB+D 120 with the cross-subject and cross-setup test setting respectively. 3DV's code is available at https://github.com/3huo/3DV-Action.
Benchmarking Adversarial Robustness on Image Classification
Authors: Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms. In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks. After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods. Based on the evaluation results, we draw several important findings that can provide insights for future research, including: 1) The relative robustness between models can change across different attack configurations, thus it is encouraged to adopt the robustness curves to evaluate adversarial robustness; 2) As one of the most effective defense techniques, adversarial training can generalize across different threat models; 3) Randomization-based defenses are more robust to query-based black-box attacks.
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients
Authors: Alvin Chan, Yi Tay, Yew-Soon Ong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Adversarial perturbations are imperceptible changes to input pixels that can change the prediction of deep learning models. Learned weights of models robust to such perturbations are previously found to be transferable across different tasks but this applies only if the model architecture for the source and target tasks is the same. Input gradients characterize how small changes at each input pixel affect the model output. Using only natural images, we show here that training a student model's input gradients to match those of a robust teacher model can gain robustness close to a strong baseline that is robustly trained from scratch. Through experiments in MNIST, CIFAR-10, CIFAR-100 and Tiny-ImageNet, we show that our proposed method, input gradient adversarial matching, can transfer robustness across different tasks and even across different model architectures. This demonstrates that directly targeting the semantics of input gradients is a feasible way towards adversarial robustness.
Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging
Authors: Xiangrui Zeng, Min Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose a Geometric unsupervised matching Net-work (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.
Deep Parametric Shape Predictions Using Distance Fields
Authors: Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representations, a tedious and potentially time-consuming process. While advances in deep learning have been successfully applied to noisy geometric data, the task of generating parametric shapes has so far been difficult for these methods. Hence, we propose a new framework for predicting parametric shape primitives using deep learning. We use distance fields to transition between shape parameters like control points and input data on a pixel grid. We demonstrate efficacy on 2D and 3D tasks, including font vectorization and surface abstraction.
Correspondence-Free Material Reconstruction using Sparse Surface Constraints
Authors: Sebastian Weiss, Robert Maier, Daniel Cremers, Rudiger Westermann, Nils Thuerey
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present a method to infer physical material parameters, and even external boundaries, from the scanned motion of a homogeneous deformable object via the solution of an inverse problem. Parameters are estimated from real-world data sources such as sparse observations from a Kinect sensor without correspondences. We introduce a novel Lagrangian-Eulerian optimization formulation, including a cost function that penalizes differences to observations during an optimization run. This formulation matches correspondence-free, sparse observations from a single-view depth image with a finite element simulation of deformable bodies. In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme.
PointPainting: Sequential Fusion for 3D Object Detection
Authors: Sourabh Vora, Alex H. Lang, Bassam Helou, Oscar Beijbom
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity for tight sensor-fusion. Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature. In this work, we propose PointPainting: a sequential fusion method to fill this gap. PointPainting works by projecting lidar points into the output of an image-only semantic segmentation network and appending the class scores to each point. The appended (painted) point cloud can then be fed to any lidar-only method. Experiments show large improvements on three different state-of-the art methods, Point-RCNN, VoxelNet and PointPillars on the KITTI and nuScenes datasets. The painted version of PointRCNN represents a new state of the art on the KITTI leaderboard for the bird's-eye view detection task. In ablation, we study how the effects of Painting depends on the quality and format of the semantic segmentation output, and demonstrate how latency can be minimized through pipelining.
Adaptive Subspaces for Few-Shot Learning
Authors: Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Object recognition requires a generalization capability to avoid overfitting, especially when the samples are extremely few. Generalization from limited samples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of life long learning. In this paper, we provide a framework for few-shot learning by introducing dynamic classifiers that are constructed from few samples. A subspace method is exploited as the central block of a dynamic classifier. We will empirically show that such modelling leads to robustness against perturbations (e.g., outliers) and yields competitive results on the task of supervised and semi-supervised few-shot classification. We also develop a discriminative form which can boost the accuracy even further. Our code is available at https://github.com/chrysts/dsn_fewshot
In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction From 2D Landmarks
Authors: Heng Yang, Luca Carlone
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image. We adopt the 3D deformable shape model and formulate the reconstruction as a joint optimization of the camera pose and the linear shape parameters. Our first contribution is to apply Lasserre's hierarchy of convex Sums-of-Squares (SOS) relaxations to solve the shape reconstruction problem and show that the SOS relaxation of minimum order 2 empirically solves the original non-convex problem exactly. Our second contribution is to exploit the structure of the polynomial in the objective function and find a reduced set of basis monomials for the SOS relaxation that significantly decreases the size of the resulting semidefinite program (SDP) without compromising its accuracy. These two contributions, to the best of our knowledge, lead to the first certifiably optimal solver for 3D shape reconstruction, that we name Shape*. Our third contribution is to add an outlier rejection layer to Shape[?] using a truncated least squares (TLS) robust cost function and leveraging graduated non-convexity to solve TLS without initialization. The result is a robust reconstruction algorithm, named Shape#, that tolerates a large amount of outlier measurements. We evaluate the performance of Shape[?] and Shape# in both simulated and real experiments, showing that Shape[?] outperforms local optimization and previous convex relaxation techniques, while Shape# achieves state-of-the-art performance and is robust against 70% outliers in the FG3DCar dataset.
Dynamic Graph Message Passing Networks
Authors: Li Zhang, Dan Xu, Anurag Arnab, Philip H.S. Torr
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although CNNs have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph is beneficial for such modelling, however, its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph. This is achieved by adaptively sampling nodes in the graph, conditioned on the input, for message passing. Based on the sampled nodes, we dynamically predict node-dependent filter weights and the affinity matrix for propagating information between them. Using this model, we show significant improvements with respect to strong, state-of-the-art baselines on three different tasks and backbone architectures. Our approach also outperforms fully-connected graphs while using substantially fewer floating-point operations and parameters.
Evaluating Weakly Supervised Object Localization Methods Right
Authors: Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, Hyunjung Shim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (CAM), the field has focused on how to expand the attention regions to cover objects more broadly and localize them better. However, these strategies rely on full localization supervision to validate hyperparameters and for model selection, which is in principle prohibited under the WSOL setup. In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set. We observe that, under our protocol, the five most recent WSOL methods have not made a major improvement over the CAM baseline. Moreover, we report that existing WSOL methods have not reached the few-shot learning baseline, where the full-supervision at validation time is used for model training instead. Based on our findings, we discuss some future directions for WSOL.
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
Authors: Jiahui Huang, Sheng Yang, Tai-Jiang Mu, Shi-Min Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynamic object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving. At the core of our system lies a multi-level probabilistic association mechanism and a heterogeneous Conditional Random Field (CRF) clustering approach combining semantic, spatial and motion information to jointly infer cluster segmentations online for every frame. The poses of camera and dynamic objects are instantly solved through a sliding-window optimization. Our system is evaluated on Oxford Multimotion and KITTI dataset both quantitatively and qualitatively, reaching comparable results to state-of-the-art solutions on both odometry and dynamic trajectory recovery.
DaST: Data-Free Substitute Training for Adversarial Attacks
Authors: Mingyi Zhou, Jing Wu, Yipeng Liu, Shuaicheng Liu, Ce Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. However, pre-trained models are hard to obtain in real-world tasks. In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data. To achieve this, DaST utilizes specially designed generative adversarial networks (GANs) to train the substitute models. In particular, we design a multi-branch architecture and label-control loss for the generative model to deal with the uneven distribution of synthetic samples. The substitute model is then trained by the synthetic samples generated by the generative model, which are labeled by the attacked model subsequently. The experiments demonstrate the substitute models produced by DaST can achieve competitive performance compared with the baseline models which are trained by the same train set with attacked models. Additionally, to evaluate the practicability of the proposed method on the real-world task, we attack an online machine learning model on the Microsoft Azure platform. The remote model misclassifies 98.35% of the adversarial examples crafted by our method. To the best of our knowledge, we are the first to train a substitute model for adversarial attacks without any real data.
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer
Authors: Fengmao Lv, Tao Liang, Xiang Chen, Guosheng Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Exploiting photo-realistic synthetic data to train semantic segmentation models has received increasing attention over the past years. However, the domain mismatch between synthetic and real images will cause a significant performance drop when the model trained with synthetic images is directly applied to real-world scenarios. In this paper, we propose a new domain adaptation approach, called Pivot Interaction Transfer (PIT). Our method mainly focuses on constructing pivot information that is common knowledge shared across domains as a bridge to promote the adaptation of semantic segmentation model from synthetic domains to real-world domains. Specifically, we first infer the image-level category information about the target images, which is then utilized to facilitate pixel-level transfer for semantic segmentation, with the assumption that the interactive relation between the image-level category information and the pixel-level semantic information is invariant across domains. To this end, we propose a novel multi-level region expansion mechanism that aligns both the image-level and pixel-level information. Comprehensive experiments on the adaptation from both GTAV and SYNTHIA to Cityscapes clearly demonstrate the superiority of our method.
Minimal Solutions for Relative Pose With a Single Affine Correspondence
Authors: Banglei Guan, Ji Zhao, Zhang Li, Fang Sun, Friedrich Fraundorfer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closed-form solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
Authors: Mang Tik Chiu, Xingqian Xu, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David Wilson, Adrian Tudor, Naira Hovakimyan, Thomas S. Huang, Honghui Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. We collected 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges Agriculture-Vision poses to both the computer vision and agriculture communities. Future versions of this dataset will include even more aerial images, anomaly patterns and image channels.
ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture
Authors: Sena Kiciroglu, Helge Rhodin, Sudipta N. Sinha, Mathieu Salzmann, Pascal Fua
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the problem that we address in this paper. Specifically, given a short video sequence, we introduce an algorithm that predicts which viewpoints should be chosen to capture future frames so as to maximize 3D human pose estimation accuracy. The key idea underlying our approach is a method to estimate the uncertainty of the 3D body pose estimates. We integrate several sources of uncertainty, originating from deep learning based regressors and temporal smoothness. Our motion planner yields improved 3D body pose estimates and outperforms or matches existing ones that are based on person following and orbiting.
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Authors: Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, Roee Litman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design. That said, deep learning based HTR is limited, as in every other task, by the number of training examples. Gathering data is a challenging and costly task, and even more so, the labeling task that follows, of which we focus here. One possible approach to reduce the burden of data annotation is semi-supervised learning. Semi supervised methods use, in addition to labeled data, some unlabeled samples to improve performance, compared to fully supervised ones. Consequently, such methods may adapt to unseen images during test time. We present ScrabbleGAN, a semi-supervised approach to synthesize handwritten text images that are versatile both in style and lexicon. ScrabbleGAN relies on a novel generative model which can generate images of words with an arbitrary length. We show how to operate our approach in a semi-supervised manner, enjoying the aforementioned benefits such as performance boost over state of the art supervised HTR. Furthermore, our generator can manipulate the resulting text style. This allows us to change, for instance, whether the text is cursive, or how thin is the pen stroke.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Authors: Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open.