pwc-archive/papers-with-abstracts huggingface dataset:
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme
Authors: Xi Yang, Wangmeng Xiang, Hui Zeng, Lei Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Super-Resolution, Video Super-Resolution

    Video super-resolution (VSR) aims to improve the spatial resolution of low-resolution (LR) videos. Existing VSR methods are mostly trained and evaluated on synthetic datasets, where the LR videos are uniformly downsampled from their high-resolution (HR) counterparts by some simple operators (e.g., bicubic downsampling). Such simple synthetic degradation models, however, cannot well describe the complex degradation processes in real-world videos, and thus the trained VSR models become ineffective in real-world applications. As an attempt to bridge the gap, we build a real-world video super-resolution (RealVSR) dataset by capturing paired LR-HR video sequences using the multi-camera system of iPhone 11 Pro Max. Since the LR-HR video pairs are captured by two separate cameras, there are inevitably certain misalignment and luminance/color differences between them. To more robustly train the VSR model and recover more details from the LR inputs, we convert the LR-HR videos into YCbCr space and decompose the luminance channel into a Laplacian pyramid, and then apply different loss functions to different components. Experiments validate that VSR models trained on our RealVSR dataset demonstrate better visual quality than those trained on synthetic datasets under real-world settings. They also exhibit good generalization capability in cross-camera tests. The dataset and code can be found at https://github.com/IanYeung/RealVSR.
BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation
Authors: Yahao Liu, Jinhong Deng, Xinchen Gao, Wen Li, Lixin Duan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

    Existing cross-domain semantic segmentation methods usually focus on the overall segmentation results of whole objects but neglect the importance of object boundaries. In this work, we find that the segmentation performance can be considerably boosted if we treat object boundaries properly. For that, we propose a novel method called BAPA-Net, which is based on a convolutional neural network via Boundary Adaptation and Prototype Alignment, under the unsupervised domain adaptation setting. Specifically, we first construct additional images by pasting objects from source images to target images, and we develop a so-called boundary adaptation module to weigh each pixel based on its distance to the nearest boundary pixel of those pasted source objects. Moreover, we pro- pose another prototype alignment module to reduce the domain mismatch by minimizing distances between the class prototypes of the source and target domains, where boundaries are removed to avoid domain confusion during prototype calculation. By integrating the boundary adaptation and prototype alignment, we are able to train a discriminative and domain-invariant model for cross-domain semantic segmentation. We conduct extensive experiments on the benchmark datasets of urban scenes (i.e., GTA5->Cityscapes and SYNTHIA->Cityscapes). And the promising results clearly show the effectiveness of our BAPA-Net method over existing state-of-the-art for cross-domain semantic segmentation. Our implementation is available at https://github.com/manmanjun/BAPA-Net.
Generalized Shuffled Linear Regression
Authors: Feiran Li, Kent Fujiwara, Fumio Okura, Yasuyuki Matsushita
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: regression

    We consider the shuffled linear regression problem where the correspondences between covariates and responses are unknown. While the existing formulation assumes an ideal underlying bijection in which all pieces of data should match, such an assumption barely holds in real-world applications due to either missing data or outliers. Therefore, in this work, we generalize the formulation of shuffled linear regression to a broader range of conditions where only part of the data should correspond. Moreover, we present a remarkably simple yet effective optimization algorithm with guaranteed global convergence. Distinct tasks validate the effectiveness of the proposed method.
Visual Graph Memory With Unsupervised Representation for Visual Navigation
Authors: Obin Kwon, Nuri Kim, Yunho Choi, Hwiyeon Yoo, Jeongho Park, Songhwai Oh
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Navigate, Visual Navigation

    We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history. The proposed VGM is constructed incrementally based on the similarities among the unsupervised representations of observed images, and these representations are learned from an unlabeled image dataset. We also propose a navigation framework that can utilize the proposed VGM to tackle visual navigation problems. By incorporating a graph convolutional network and the attention mechanism, the proposed agent refers to the VGM to navigate the environment while simultaneously building the VGM. Using the VGM, the agent can embed its navigation history and other useful task-related information. We validate our approach on the visual navigation tasks using the Habitat simulator with the Gibson dataset, which provides a photo-realistic simulation environment. The extensive experimental results show that the proposed navigation agent with VGM surpasses the state-of-the-art approaches on image-goal navigation tasks.
Statistically Consistent Saliency Estimation
Authors: Shunyan Luo, Emre Barut, Fang Jin
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Computational Efficiency, Saliency Prediction

    The growing use of deep learning for a wide range of data problems has highlighted the need to understand and diagnose these models appropriately, making deep learning interpretation techniques an essential tool for data analysts. The numerous model interpretation methods proposed in recent years are generally based on heuristics, with little or no theoretical guarantees. Here we present a statistical framework for saliency estimation for black-box computer vision models. Our proposed model-agnostic estimation procedure, which is statistically consistent and capable of passing saliency checks, has polynomial-time computational efficiency since it only requires solving a linear program. An upper bound is established on the number of model evaluations needed to recover regions of importance with high probability through our theoretical analysis. Furthermore, a new perturbation scheme is presented for the estimation of local gradients that is more efficient than commonly used random perturbation schemes. The validity and excellence of our new method are demonstrated experimentally using sensitivity analysis on multiple datasets.
Towards Robustness of Deep Neural Networks via Regularization
Authors: Yao Li, Martin Renqiang Min, Thomas Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Attack, Adversarial Robustness

    Recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples. Inspired by the observation that adversarial examples often lie outside the natural image data manifold and the intrinsic dimension of image data is much smaller than its pixel space dimension, we propose to embed high-dimensional input images into a low-dimensional space and apply regularization on the embedding space to push the adversarial examples back to the manifold. The proposed framework is called Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization. Besides improving classification accuracy against adversarial examples, the framework can be combined with detection methods to detect adversarial examples. Experimental results on several benchmark datasets show that, our proposed framework achieves good performance against strong adversarial attack methods.
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification
Authors: Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Domain Generalization

    Most existing few-shot classification methods only consider generalization on one dataset (i.e., single-domain), failing to transfer across various seen and unseen domains. In this paper, we consider the more realistic multi-domain few-shot classification problem to investigate the cross-domain generalization. Two challenges exist in this new setting: (1) how to efficiently generate multi-domain feature representation, and (2) how to explore domain correlations for better cross-domain generalization. We propose a parameter-efficient multi-mode modulator to address both challenges. First, the modulator is designed to maintain multiple modulation parameters (one for each domain) in a single network, thus achieving single-network multi-domain representation. Given a particular domain, domain-aware features can be efficiently generated with the well-devised separative selection module and cooperative query module. Second, we further divide the modulation parameters into the domain-specific set and the domain-cooperative set to explore the intra-domain information and inter-domain correlations, respectively. The intra-domain information describes each domain independently to prevent negative interference. The inter-domain correlations guide information sharing among relevant domains to enrich their own representation. Moreover, unseen domains can utilize the correlations to obtain an adaptive combination of seen domains for extrapolation. We demonstrate that the proposed multi-mode modulator achieves state-of-the-art results on the challenging META-DATASET benchmark, especially for unseen test domains.
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion
Authors: Bin Xiao, Haifeng Wu, Xiuli Bi
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Computational Efficiency, Deep Learning, Multi Focus Image Fusion

    Compared with traditional methods, the deep learning-based multi-focus image fusion methods can effectively improve the performance of image fusion tasks. However, the existing deep learning-based methods encounter a common issue of a large number of parameters, which leads to the deep learning models with high time complexity and low fusion efficiency. To address this issue, we propose a novel discrete Tchebichef moment-based Deep neural network, termed as DTMNet, for multi-focus image fusion. The proposed DTMNet is an end-to-end deep neural network with only one convolutional layer and three fully connected layers. The convolutional layer is fixed with DTM coefficients (DTMConv) to extract high/low-frequency information without learning parameters effectively. The three fully connected layers have learnable parameters for feature classification. Therefore, the proposed DTMNet for multi-focus image fusion has a small number of parameters (0.01M paras vs. 4.93M paras of regular CNN) and high computational efficiency (0.32s vs. 79.09s by regular CNN to fuse an image). In addition, a large-scale multi-focus image dataset is synthesized for training and verifying the deep learning model. Experimental results on three public datasets demonstrate that the proposed method is competitive with or even outperforms the state-of-the-art multi-focus image fusion methods in terms of subjective visual perception and objective evaluation metrics.
Adaptive Surface Reconstruction With Multiscale Convolutional Kernels
Authors: Benjamin Ummenhofer, Vladlen Koltun
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction, Surface Reconstruction

    We propose generalized convolutional kernels for 3D reconstruction with ConvNets from point clouds. Our method uses multiscale convolutional kernels that can be applied to adaptive grids as generated with octrees. In addition to standard kernels in which each element has a distinct spatial location relative to the center, our elements have a distinct relative location as well as a relative scale level. Making our kernels span multiple resolutions allows us to apply ConvNets to adaptive grids for large problem sizes where the input data is sparse but the entire domain needs to be processed. Our ConvNet architecture can predict the signed and unsigned distance fields for large data sets with millions of input points and is faster and more accurate than classic energy minimization or recent learning approaches. We demonstrate this in a zero-shot setting where we only train on synthetic data and evaluate on the Tanks and Temples dataset of real-world large-scale 3D scenes.
DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection
Authors: Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Anomaly Detection, Anomaly Localization, Defect Detection

    Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRAEM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-the-art unsupervised methods by a large margin and even delivers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces
Authors: Jinhui Xiong, Wolfgang Heidrich
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction

    We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water. We propose a novel differentiable framework, which, to our knowledge, is the first single-camera solution that is capable of simultaneously retrieving the structure of dynamic water surfaces and static underwater scene geometry in the wild. This framework integrates ray casting of Snell's law at the refractive interface, multi-view triangulation and specially designed loss functions. Our method is calibration-free, and thus it is easy to collect data outdoors in uncontrolled environments. Experimental results show that our method is able to realize robust and quality reconstructions on a variety of scenes, both in a laboratory environment and in the wild, and even in a salt water environment. We believe the method is promising for applications in surveying and environmental monitoring.
Pose Correction for Highly Accurate Visual Localization in Large-Scale Indoor Spaces
Authors: Janghun Hyeon, Joohyung Kim, Nakju Doh
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Retrieval, Mixed Reality, Pose Estimation, Retrieval, Visual Localization

    Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps: image retrieval, pose estimation, and pose selection. However, further research is needed to improve the accuracy of large-scale indoor visual localization. We demonstrate that the limitations in the previous methods can be attributed to the sparsity of image positions in the database, which causes view-differences between a query and a retrieved image from the database. In this paper, to address this problem, we propose a novel module, named pose correction, that enables re-estimation of the pose with local feature matching in a similar view by reorganizing the local features. This module enhances the accuracy of the initially estimated pose and assigns more reliable ranks. Furthermore, the proposed method achieves a new state-of-the-art performance with an accuracy of more than 90% within 1.0m in the challenging indoor benchmark dataset InLoc for the first time.
LIRA: Learnable, Imperceptible and Robust Backdoor Attacks
Authors: Khoa Doan, Yingjie Lao, Weijie Zhao, Ping Li
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Backdoor Attack, backdoor defense, Stochastic Optimization

    Recently, machine learning models have demonstrated to be vulnerable to backdoor attacks, primarily due to the lack of transparency in black-box models such as deep neural networks. A third-party model can be poisoned such that it works adequately in normal conditions but behaves maliciously on samples with specific trigger patterns. However, the trigger injection function is manually defined in most existing backdoor attack methods, e.g., placing a small patch of pixels on an image or slightly deforming the image before poisoning the model. This results in a two-stage approach with a sub-optimal attack success rate and a lack of complete stealthiness under human inspection. In this paper, we propose a novel and stealthy backdoor attack framework, LIRA, which jointly learns the optimal, stealthy trigger injection function and poisons the model. We formulate such an objective as a non-convex, constrained optimization problem. Under this optimization framework, the trigger generator function will learn to manipulate the input with imperceptible noise to preserve the model performance on the clean data and maximize the attack success rate on the poisoned data. Then, we solve this challenging optimization problem with an efficient, two-stage stochastic optimization procedure. Finally, the proposed attack framework achieves 100% success rates in several benchmark datasets, including MNIST, CIFAR10, GTSRB, and T-ImageNet, while simultaneously bypassing existing backdoor defense methods and human inspection.
Orthographic-Perspective Epipolar Geometry
Authors: Viktor Larsson, Marc Pollefeys, Magnus Oskarsson
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Camera Calibration

    In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many applications. They can e.g. model 2D maps (such as floor plans), aerial/satellite photography and even approximate narrow field-of-view cameras (e.g. from telephoto lenses). In our experiments we highlight various applications of the developed theory and solvers, including Radar-Camera calibration and aligning Structure-from-Motion models to aerial or satellite images.
Scribble-Supervised Semantic Segmentation Inference
Authors: Jingshan Xu, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Segmentation, Semantic Segmentation

    In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation. In virtue of latent contextual dependency, we encapsulate two crucial cues, contextual pattern propagation and semantic label diffusion, to enhance and refine pixel-level segmentation results from partially known seeds. In contextual pattern propagation, different-granular contextual patterns are correlated and leveraged to properly diffuse pattern information based on graphical model, so as to increase the inference confidence of pixel label prediction. Further, depending on high confidence scores of estimated pixels, the initial annotated seeds are progressively spread over the image through dynamically learning an adaptive decision strategy. The two cues are finally modularized to form a close-looping update process during pixel-wise label inference. Extensive experiments demonstrate that our proposed progressive segmentation inference can benefit from the combination of spatial and semantic context cues, and meantime achieve the state-of-the-art performance on two public scribble segmentation datasets.
Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification
Authors: Ziyu Wei, Xi Yang, Nannan Wang, Xinbo Gao
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Person Re-Identification

    Visible infrared person re-identification (VI-REID) aims to match pedestrian images between the daytime visible and nighttime infrared camera views. The large cross-modality discrepancies have become the bottleneck which limits the performance of VI-REID. Existing methods mainly focus on capturing cross-modality sharable representations by learning an identity classifier. However, the heterogeneous pedestrian images taken by different spectrum cameras differ significantly in image styles, resulting in inferior discriminability of feature representations. To alleviate the above problem, this paper explores the correlation between two modalities and proposes a novel syncretic modality collaborative learning (SMCL) model to bridge the cross-modality gap. A new modality that incorporates features of heterogeneous images is constructed automatically to steer the generation of modality-invariant representations. Challenge enhanced homogeneity learning (CEHL) and auxiliary distributional similarity learning (ADSL) are integrated to project heterogeneous features on a unified space and enlarge the inter-class disparity, thus strengthening the discriminative power. Extensive experiments on two cross-modality benchmarks demonstrate the effectiveness and superiority of the proposed method. Especially, on SYSU-MM01 dataset, our SMCL model achieves 67.39% rank-1 accuracy and 61.78% mAP, surpassing the cutting-edge works by a large margin.