Due to the difficulty of solving the matting problem, lots of methods use some kinds of assistance to acquire high quality alpha matte. Green screen matting methods rely on physical equipment. Trimap-based methods take manual interactions as external input. Background-based methods require a pre-captured, static background. The methods are not flexible and convenient enough to use widely. Trimap-free methods are flexible but not stable in complicated video applications. To be stable and flexible in real applications, we propose an adaptive background matting method. The user first captures their videos freely, moving the cameras. Then the user captures the background video afterwards, roughly covering the previous captured regions. We use dynamic background video instead of static background for accurate matting. The proposed method is convenient to use in any scenes as the static camera and background is no more the limitation. To achieve this goal, we use background matching network to find the best-matched background frame by frame from dynamic backgrounds. Then, robust semantic estimation network is used to estimate the coarse alpha matte. Finally, we crop and zoom the target region according to the coarse alpha matte, and estimate the final accurate alpha matte. In experiments, the proposed method is able to perform comparably against the state-of-the-art matting methods.
State Key Laboratory of Software Engineering, Wuhan University（武汉大学软件工程国家重点实验室）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Closed Form is a propagation based matting algorithm, functioning well on images with good propagation . The deficiency of the Closed Form method is that for complex areas with poor image propagation , such as hole areas or areas of long and narrow structures. The right results are usually hard to get. On these areas, if certain flags are provided, it can improve the effects of matting. In this paper, we design a matting algorithm by local sampling and the KNN classifier propagation based matting algorithm. First of all, build the corresponding features space according to the different compone…
Memory Efficient Matting with Adaptive Token Routing
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Visual Intelligence + X International Joint Laboratory of the Ministry of Education（教育部视觉智能+X国际联合实验室）；Pengcheng Laboratory（鹏城实验室）；MT Lab, Meitu Inc.（美图公司MT实验室）
Dalian University of Technology（大连理工大学）；Beijing Technology And Business University（北京工商大学）；Wonxing Technology（万兴科技）
Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details. However, we argue that appearance-agnostic integration can only provide biased foreground details and alpha mattes require different-level feature aggregation for better pixel-wise opacity perception. In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input. S…
Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation
Korea Advanced Institute of Science \& Technology School of Electrical Engineering Daejeon Republic of Korea；Kyungpook National University School of Computer Science；Korea Advanced Institute of Science \& Technology；Kyungpook National University
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；College of Geomatics, Shandong University of Science and Technology（山东科技大学测绘学院）；Shenzhen Research Institute, Shandong University（山东大学深圳研究院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy. However, it is unrealistic to increase the size of the image patch size without restriction. Arbitrarily extending the patch size will change the local stereo matching method into the global stereo matching method, and the matching accuracy will be saturated. We simplified the existing Siamese convolutional network by reducing the number of network parameters and propose an efficient CNN based structure, namely Adaptive Deconvolution-based disparity matching Net (ADSM net) …
Tencent YouTu Lab（腾讯优图实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Shanghai Jiao Tong University（上海交通大学）
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (\ie, one-to-one assignment) in patch-level matching.Accordingly, we introduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while est…
University of Science and Technology of China（中国科学技术大学）
Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregati…
SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）
Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignm…
Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-conditional distributions by narrowing down cross-domain prototypes (class centers). Though great success,they ignore the significant within-class variance and the domain-mismatched semantics within the training batch, leading to a sub-optimal adaptation. To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the a…
Gwangju Institute of Science and Technology（光州科学技术院）
In this paper, we propose the methods to handle temporal errors during multi-object tracking. Temporal error occurs when objects are occluded or noisy detections appear near the object. In those situations, tracking may fail and various errors like drift or ID-switching occur. It is hard to overcome temporal errors only by using motion and shape information. So, we propose the historical appearance matching method and joint-input siamese network which was trained by 2-step process. It can prevent tracking failures although objects are temporally occluded or last matching information is unrelia…
Generating robust and reliable correspondences across images is a fundamental task for a diversity of applications. To capture context at both global and local granularity, we propose ASpanFormer, a Transformer-based detector-free matcher that is built on hierarchical attention structure, adopting a novel attention operation which is capable of adjusting attention span in a self-adaptive manner. To achieve this goal, first, flow maps are regressed in each cross attention phase to locate the center of search region. Next, a sampling grid is generated around the center, whose size, instead of be…
State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained. In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. In addition, variances …
Dalian University of Technology（大连理工大学）；Beijing Institute of Technology（北京理工大学）；The University of Sydney（悉尼大学）
Being a crucial task of autonomous driving, Stereo matching has made great progress in recent years. Existing stereo matching methods estimate disparity instead of depth. They treat the disparity errors as the evaluation metric of the depth estimation errors, since the depth can be calculated from the disparity according to the triangulation principle. However, we find that the error of the depth depends not only on the error of the disparity but also on the depth range of the points. Therefore, even if the disparity error is low, the depth error is still large, especially for the distant poin…
FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching
Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）；Penn Image Computing and Science Laboratory, University of Pennsylvania（宾夕法尼亚大学宾夕法尼亚图像计算与科学实验室）；Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电子与系统工程系）；Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学放射学系）
SenseTime Group Limited（商汤科技集团）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Chinese University of Hong Kong（香港中文大学）；ShanghaiTech University（上海科技大学）
Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite limited. Addressing such problem, we present a novel domain-adaptive approach called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design a…
Seoul National University（首尔大学）；Facebook, Inc.（Facebook公司）
Semi-supervised video object segmentation (semi-VOS) is widely used in many applications. This task is tracking class-agnostic objects from a given target mask. For doing this, various approaches have been developed based on online-learning, memory networks, and optical flow. These methods show high accuracy but are hard to be utilized in real-world applications due to slow inference time and tremendous complexity. To resolve this problem, template matching methods are devised for fast processing speed but sacrificing lots of performance in previous models. We introduce a novel semi-VOS model …
Shanghai Jiao Tong University（上海交通大学）；University College London（伦敦大学学院）；Shanghai Renji Hospital, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属上海仁济医院）；University of the West of England（西英格兰大学）
For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images. Most existing works depend heavily on the outlier segmentation to remove the outlier effect in the registration. Moreover, these works do not handle simultaneously the missing correspondences and local large deformations. In this paper, we defined the nonrigid image registration as a local adaptive kernel regression which locally reconstruct the…
In this work, our objective is to address the problems of generalization and flexibility for text recognition in documents. We introduce a new model that exploits the repetitive nature of characters in languages, and decouples the visual representation learning and linguistic modelling stages. By doing this, we turn text recognition into a shape matching problem, and thereby achieve generalization in appearance and flexibility in classes. We evaluate the new model on both synthetic and real datasets across different alphabets and show that it can handle challenges that traditional architecture…
Megvii Research（旷视研究院）；Tencent（腾讯）；University of Electronic Science and Technology of China（电子科技大学）
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network wit…
Matching deformable objects using their shapes is an important problem in computer vision since shape is perhaps the most distinguishable characteristic of an object. The problem is difficult due to many factors such as intra-class variations, local deformations, articulations, viewpoint changes and missed and extraneous contour portions due to errors in shape extraction. While small local deformations has been handled in the literature by allowing some leeway in the matching of individual contour points via methods such as Chamfer distance and Hausdorff distance, handling more severe deformat…
Australian National University（澳大利亚国立大学）；Tsinghua University（清华大学）
Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might not be the best choice given the difference in matching scopes between re-ID and MTMCT problems. Re-ID systems focus on global matching, which retrieves targets from all cameras and all times. In contrast, data association in tracking is a local matching problem, since its candidates only come from neighboring locations and time frames. In this paper, we design experiments to verify such misfit between global re-ID …
Louisiana State University（路易斯安那州立大学）；Northeastern University（东北大学）
We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize information in the continuously growing feature bank remains under-explored, and this leads to inefficient design of the bank. We introduce an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features. We also design a new confidence loss an…
Nanjing University of Information Science and Technology（南京信息工程大学）；Beijing Electro-Mechanical Engineering Institute（北京机电工程研究所）；University of Extremadura（埃斯特雷马杜拉大学）
Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learn…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Inception Institute of Artificial Intelligence (IIAI)（启程人工智能研究院）
For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit ma…
Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising
School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）；National Center for Applied Mathematics Beijing, Capital Normal University（首都师范大学北京国家应用数学中心）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）；Institute of Nuclear and New Energy Technology, Tsinghua University（清华大学核能与新能源技术研究院）
MAFNet:Multi-frequency Adaptive Fusion Network for Real-time Stereo Matching
College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（上海智能自主系统研究院，同济大学）；Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光学精密工程研究所）；Shanghai Integrated Innovation Center for Manned Lunar Exploration（上海载人月球探测创新中心）；Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration（上海行星测绘与深空探测遥感重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Efficient real-time disparity estimation is critical for the application of stereo vision systems in various areas. Recently, stereo network based on coarse-to-fine method has largely relieved the memory constraints and speed limitations of large-scale network models. Nevertheless, all of the previous coarse-to-fine designs employ constant offsets and three or more stages to progressively refine the coarse disparity map, still resulting in unsatisfactory computation accuracy and inference time when deployed on mobile devices. This paper claims that the coarse matching errors can be corrected e…
We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive …
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training. In this case, traditional unsupervised domain adaptation models usually fail since they cannot adapt to the target domain with over-fitting to one (or few) target samples. To address this problem, existing OSUDA methods usually integrate a style-transfer module to perform domain randomization based on the unlabeled target sample, with which multiple domains around the target sample can be explored during trai…
The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）；Beihang University（北京航空航天大学）
Text-image cross-modal retrieval is a challenging task in the field of language and vision. Most previous approaches independently embed images and sentences into a joint embedding space and compare their similarities. However, previous approaches rarely explore the interactions between images and sentences before calculating similarities in the joint space. Intuitively, when matching between images and sentences, human beings would alternatively attend to regions in images and words in sentences, and select the most salient information considering the interaction between both modalities. In t…
Existing image-text matching approaches typically leverage triplet loss with online hard negatives to train the model. For each image or text anchor in a training mini-batch, the model is trained to distinguish between a positive and the most confusing negative of the anchor mined from the mini-batch (i.e. online hard negative). This strategy improves the model's capacity to discover fine-grained correspondences and non-correspondences between image and text inputs. However, the above approach has the following drawbacks: (1) the negative selection strategy still provides limited chances for t…
Nanjing University of Information Science and Technology（南京信息工程大学）；Inria（法国国家信息与自动化研究所）；Université Paris-Saclay（巴黎萨克雷大学）；Zenith Ai；East China Normal University（华东师范大学）
Unsupervised domain adaption (UDA) is a transfer learning task where the data and annotations of the source domain are available but only have access to the unlabeled target data during training. Most previous methods try to minimise the domain gap by performing distribution alignment between the source and target domains, which has a notable limitation, i.e., operating at the domain level, but neglecting the sample-level differences. To mitigate this weakness, we propose to improve the unsupervised domain adaptation task with an inter-domain sample matching scheme. We apply the widely-used an…
Megvii Technology（旷视科技）；Group 42（G42集团）；University of Electronic Science and Technology of China（电子科技大学）
Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key …
University of Science and Technology of China（中国科学技术大学）
Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on costly 3D convolutions, the cubic computational complexity and high memory consumption make it quite expensive to deploy in real-world applications. In this paper, we aim at completely replacing the commonly used 3D convolutions to achieve fast inference speed while maintaining comparable accuracy. To this end, we first propose a sparse points based intra-scale cost aggregation method to alleviate the well-known edge-f…
The University of Queensland（昆士兰大学）；The University of Adelaide（阿德莱德大学）
Instance retrieval requires one to search for images that contain a particular object within a large corpus. Recent studies show that using image features generated by pooling convolutional layer feature maps (CFMs) of a pretrained convolutional neural network (CNN) leads to promising performance for this task. However, due to the global pooling strategy adopted in those works, the generated image feature is less robust to image clutter and tends to be contaminated by the irrelevant image patterns. In this article, we alleviate this drawback by proposing a novel reranking algorithm using CFMs …
Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching
College of Aerospace Science and Engineering（航空航天科学与工程学院）；National University of Defense Technology（国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）
The University of Tokyo（东京大学）；KDDI R&D Laboratories, Inc.（KDDI研究与开发实验室）；National Institute of Informatics（信息学研究所）
In this paper, we propose a stand-alone mobile visual search system based on binary features and the bag-of-visual words framework. The contribution of this study is three-fold: (1) We propose an adaptive substring extraction method that adaptively extracts informative bits from the original binary vector and stores them in the inverted index. These substrings are used to refine visual word-based matching. (2) A modified local NBNN scoring method is proposed in the context of image retrieval, which considers the density of binary features in scoring each feature matching. (3) In order to suppr…
Université de Sherbrooke（舍布鲁克大学）；Laboratoire Bordelais de Recherche en Informatique（波尔多计算机研究实验室）
Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration meth…
