Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality. Structure typically appears in the form of individual parts and their arrangement. Knowing about object structure can be an important cue for object recognition and scene understanding - a key goal for various AR and robotics applications. However, commodity RGB-D sensors used in these scenarios only produce raw, unorganized point clouds, without structural information about the captured scene. Moreover, the generated data is commonly partial and susceptible to artifacts and noise, which makes inferring the structure of scanned objects challenging. In this paper, we organize large shape collections into parameterized shape templates to capture the underlying structure of the objects. The templates allow us to transfer the structural information onto new objects and incomplete scans. We employ a deep neural network that matches the partial scan with one of the shape templates, then match and fit it to complete and detailed models from the collection. This allows us to faithfully label its parts and to guide the reconstruction of the scanned object. We showcase the effectiveness of our method by comparing it to other state-of-the-art approaches.
Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）；SYSU-CMU Shunde International Joint Research Institute（中山大学-卡内基梅隆大学顺德国际联合研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an Active Template Regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsi…
Brigham Young University（杨百翰大学）；Adobe Research（奥多比研究院）
Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts. This is even more challenging for historical forms due to noise and degradation. A crucial part of the extraction process is associating input text with pre-printed labels. We present a learned, template-free solution to detecting pre-printed text and input text/handwriting and predicting pair-wise relationships between them. While previous approaches to this problem have been focused on clean images and clear layouts, we show our approach is effective in the domain of noisy, …
Cranberry-Lemon University（蔓越莓柠檬大学）；University of the Witwatersrand（威特沃特斯兰德大学）
We propose a new method for fine-grained few-shot recognition via deep object parsing. In our framework, an object is made up of K distinct parts and for each part, we learn a dictionary of templates, which is shared across all instances and categories. An object is parsed by estimating the locations of these K parts and a set of active templates that can reconstruct the part features. We recognize test instances by comparing its active templates and the relative geometry of its part locations against those of the presented few-shot instances. Our method is end-to-end trainable to learn part t…
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；Noah’s Ark Lab Huawei Technologies（华为技术有限公司诺亚方舟实验室）
How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. In order to solve this problem, this work proposes a statistical method based on the output probability map to calculate the pixel quality information, which is called pixel score. In addition, the Quality-Aware Module (QAM) is proposed to fuse the different quality information, the purpose of which is to estimate the quality of human parsing results. We combine QAM with a concise and effective network design to propose Quality-Aware Network (QANe…
University of Electronic Science and Technology of China（电子科技大学）
Existing methods of multiple human parsing usually adopt a two-stage strategy (typically top-down and bottom-up), which suffers from either strong dependence on prior detection or highly computational redundancy during post-grouping. In this work, we present an end-to-end multiple human parsing framework using representative parts, termed RepParser. Different from mainstream methods, RepParser solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping.To this end, RepParser decouples the parsing pipeline into instance-aware kernel gene…
University of Chinese Academy of Sciences（中国科学院大学）；Megvii Inc（旷视科技）；University of Science and Technology of China（中国科学技术大学）；Peng Cheng Laboratory（鹏城实验室）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance distance caused by different views and the subtle inter-instance discrepancy caused by similar vehicles. In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. First, we introduce a parsing network to parse a vehicle into four different views, and then align the features by mask average pooling. Such alignment provides a f…
Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；Tencent AI Lab（腾讯人工智能实验室）；University of Trento（特伦托大学）；Adobe Research（Adobe研究院）；AI Institute（360人工智能研究院）
Parsing urban scene images benefits many applications, especially self-driving. Most of the current solutions employ generic image parsing models that treat all scales and locations in the images equally and do not consider the geometry property of car-captured urban scene images. Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors. In this work, we propose a new FoveaNet model to fully exploit the perspective geometry of scen…
As a fine-grained segmentation task, human parsing is still faced with two challenges: inter-part indistinction and intra-part inconsistency, due to the ambiguous definitions and confusing relationships between similar human parts. To tackle these two problems, this paper proposes a novel \textit{Affinity-aware Compression and Expansion} Network (ACENet), which mainly consists of two modules: Local Compression Module (LCM) and Global Expansion Module (GEM). Specifically, LCM compresses parts-correlation information through structural skeleton points, obtained from an extra skeleton branch. It …
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；JD AI Research（京东人工智能研究院）
Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which however overlook the correlation among different face regions. The correlation is a critical clue about the facial appearance, pose, expression etc., and should be taken into account for face parsing. To this end, we propose to model and reason the region-wise relations by learning graph representations, and leverage the edge information between regions for optimized abstraction. Specifically, we encode a facial image onto…
ETH Zurich（苏黎世联邦理工学院）；Beihang University（北京航空航天大学）；University of Technology Sydney（悉尼科技大学）
To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human semantics with sparse keypoints, is learnt and progressively improved over the network feature pyram…
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Effectively parsing the facade is essential to 3D building reconstruction, which is an important computer vision problem with a large amount of applications in high precision map for navigation, computer aided design, and city generation for digital entertainments. To this end, the key is how to obtain the shape grammars from 2D images accurately and efficiently. Although enjoying the merits of promising results on the semantic parsing, deep learning methods cannot directly make use of the architectural rules, which play an important role for man-made structures. In this paper, we present a no…
PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing
School of Computer and Information Engineering, Henan University of Economics and Law（河南财经政法大学计算机与信息工程学院）；Third Dimension (Henan) Software Technology（第三维度（河南）软件科技公司）
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Tencent（腾讯）；Nankai University（南开大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University of Science and Technology（南京理工大学）
PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution
Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)（计算机科学与技术学院，齐鲁工业大学（山东省科学院））；Shandong University of Science and Technology（山东科技大学）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东省人工智能研究院，齐鲁工业大学（山东省科学院））；School of Electronics and Information Engineering, Harbin Institute of Technology（电子与信息工程学院，哈尔滨工业大学（深圳））；School of Computer Science and Technology, Shandong University（计算机科学与技术学院，山东大学）
Johns Hopkins University（约翰斯·霍普金斯大学）；NEC Labs America（NEC美国实验室）；UC San Diego（加州大学圣地亚哥分校）
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and rele…
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc. We propose a novel quality-aware template matching method, QATM, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily embedded into any deep neural network. Specifically, we assess the quality of a matching pair using soft-ranking among all matching pairs, and thus different matching scenarios such as 1-to-1, 1-to-many, and many-to-many will be all reflected to different values. Our e…
Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing
Zhengzhou University（郑州大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）
The Chinese University of Hong Kong（香港中文大学）；Hunan University（湖南大学）
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insight is to decouple the complex mesh reconstruction into two sub-tasks: topology formulation and shape deformation. Thanks to the decoupling, DT-Net implicitly learns a disentangled representation for the topology and shape in the latent space. Hence, it can enable novel disentangled…
The University of Hong Kong（香港大学）；Harbin Institute of Technology（哈尔滨工业大学）；DAMO Academy Alibaba Group（阿里巴巴达摩院）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）
Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images. In this paper, we propose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration. Specifically, instead of using an encoder-decoder framework as previous methods, we formulate the restoration of LQ face images as a multi-scale progressive restoration procedure through semantic-aware style transformation. Given a pair of LQ…
Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks. This paper proposes a simple and effective capsule autoencoder to address this issue, called DPR-CAE. In our approach, the encoder parses the input into a set of part capsules, including pose, intensity, and dynamic vector. The decoder introduces a novel dynamic part representation (DPR) by combining the dynamic vector and a shared template bank. These part representations are then regulated by corresponding capsules to composite the final output in an interpretab…
An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机科学学院）；Faculty of Mechanical Engineering and Mechanics, Ningbo University（宁波大学机械与力学学院）
Zhejiang University of Science & Technology（浙江科技大学）
RGB thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed …
Eindhoven University of Technology（埃因霍温理工大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation…
Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA sc…
The University of Tokyo（东京大学）；RIKEN AIP（理化学研究所先进智能项目中心）
Articulated objects exist widely in the real world. However, previous 3D generative methods for unsupervised part decomposition are unsuitable for such objects, because they assume a spatially fixed part location, resulting in inconsistent part parsing. In this paper, we propose PPD (unsupervised Pose-aware Part Decomposition) to address a novel setting that explicitly targets man-made articulated objects with mechanical joints, considering the part poses. We show that category-common prior learning for both part shapes and poses facilitates the unsupervised learning of (1) part decomposition …
Weizmann Institute of Science（魏茨曼科学研究所）；UCLA Vision Lab（加州大学洛杉矶分校视觉实验室）；Northrop Grumman（诺斯罗普·格鲁曼公司）
We present a novel approach to template matching that is efficient, can handle partial occlusions, and comes with provable performance guarantees. A key component of the method is a reduction that transforms the problem of searching a nearest neighbor among $N$ high-dimensional vectors, to searching neighbors among two sets of order $\sqrt{N}$ vectors, which can be found efficiently using range search techniques. This allows for a quadratic improvement in search complexity, and makes the method scalable in handling large search spaces. The second contribution is a hashing scheme based on conse…
FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
Shenzhen University（深圳大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Tianjin University of Technology（天津理工大学）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Georgia Tech Shenzhen Institute, Tianjin University（天津大学佐治亚理工深圳学院）；Sun Yat-sen University（中山大学）；Futurewei Technologies（未来技术公司）；Tsinghua University（清华大学）
Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyp…
Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）；East China Normal University（华东师范大学）；Stanford University（斯坦福大学）
Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions. In this work, we aim to address the night-time scene parsing (NTSP) problem, which has two main challenges: 1) labeled night-time data are scarce, and 2) over- and under-exposures may co-occur in the input night-time images and are not explicitly modeled in existing pipelines. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named {\it NightCity}, of 4,297 real night-time images with ground truth pixel…
Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby. The depth gating signal is provided by stereo disparity or estimated directly from monocular input. We integrate this depth-aware gati…
Exploring the Robustness of Human Parsers Towards Common Corruptions
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Sun Yat-sen University（中山大学）；Westlake University（西湖大学）；OPPO；Tsinghua University（清华大学）
Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（机器智能与先进计算教育部重点实验室）
Sun Yat-sen University（中山大学）；SenseTime Group (Limited)（商汤集团有限公司）；CVTE Research（视源研究院）
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the "parsing-by-detection" pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially. Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results. In this w…
National University of Singapore（新加坡国立大学）；National University of Defense Technology（中国人民解放军国防科技大学）；Qihoo 360 AI Institute（奇虎360人工智能研究院）
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database "Multi-Human Parsing (MHP)" for algorithm development and e…
TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（视觉、语音和信号处理中心（CVSSP），萨里大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（计算机科学与人工智能学院，武汉理工大学）；National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，北京大学智能科学与技术学院）；College of Information and Electrical Engineering, China Agricultural University（信息与电子工程学院，中国农业大学）
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry information, typically paired depth maps, we present a new approach, a 3D-to-2D distillation framework, that enables us to leverage 3D features extracted from large-scale 3D data repository (e.g., ScanNet-v2) to enhance 2D features extracted from RGB images. Our work has three novel contributions. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2…
Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their internal structures, relate to high-level semantics, which may lead to benefits in multiple aspects, e.g. interpretable predictions and even new methods that can take the recognition performances to a next level. Towards this goal, we construct TAPOS, a new dataset developed on sp…
Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation. While part localization has shown significant success, it uses either naıve position-based partitions or static feature templates. These, however, hypothesize the pre-existence of the parts in a given image or their positions, ignoring the input image-specific information which limits their usability in challenging scenarios such as Re-ID with partial occlusions and partial probe images. In this paper, w…
