Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks334 from public data
- Deep High-Resolution Representation Learning for Visual Recognition5,145
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
- CCNet: Criss-Cross Attention for Semantic Segmentation3,111
For each pixel, a novel criss-cross attention module in CCNet harvests the contextual information of all the pixels on its criss-cross path by taking a further recurrent operation, each pixel can finally capture the full-image dependencies from all pixels.
- ByteTrack: Multi-object Tracking by Associating Every Detection Box2,841
A simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones, named ByteTrack, which achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
- Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model2,242
This paper proposes a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models and has great potential to be the next-generation backbone for vision foundation models.
- 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering1,670
This work proposes 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame, and achieves real-time rendering under high resolutions, 82 FPS at an 800x800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state- of-the-art methods.
- YOLO-World: Real-Time Open-Vocabulary Object Detection1,134
YOLO-World is introduced, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets and proposes a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information.
- EVA: Exploring the Limits of Masked Visual Representation Learning at Scale1,054
Initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models.
- Mask Scoring R-CNN1,049
This paper proposes Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks and calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation.
- TextBoxes: A Fast Text Detector with a Single Deep Neural Network955
An end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
- 905
- VAD: Vectorized Scene Representation for Efficient Autonomous Driving866
VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation, achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin.
- Robust Scene Text Recognition with Automatic Rectification693
RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text, which is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems.
- 615
- EVA-02: A visual representation for neon genesis608
EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling, is launched, demonstrating superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets.
- 595
- 567
- Revisiting multiple instance neural networks533
This article revisit Multiple Instance Neural Networks (MINNs) that the neural networks aim at solving the MIL problems and proposes a new type of MINN to learn bag representations, which is different from the existing MINNs that focus on estimating instance label.
- YOLOP: You Only Look Once for Panoptic Driving Perception495
To the best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS), and maintain excellent accuracy.
- Multiple Instance Detection Network with Online Instance Classifier Refinement494
This work formulate weakly supervised object detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes and instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online.
- Fast Dynamic Radiance Fields with Time-Aware Neural Voxels492
This framework significantly accelerates the optimization of dynamic radiance fields while maintaining high rendering quality and completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.
- MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction454
A unified permutation-equivalent modeling approach is proposed, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
- DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving439
A novel truncated diffusion policy is proposed that incorporates prior multi-mode anchors and truncates the diffusion schedule, enabling the model to learn denoising from anchored Gaussian distribution to the multi-mode driving action distribution.
- Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification410
A novel deep network called Mancs is proposed that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation.
- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection408
This paper first shows that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then shows that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method.
- Unsupervised domain adaptive re-identification: Theory and practice376
A novel self-training scheme for unsupervised domain adaptive re-identification (re-ID) that iteratively makes guesses for unlabeled target data based on an encoder and trains the encoder based on the guessed labels.
- Instances as Queries362
For the first time, it is demonstrated that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks.
- 351
- GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models339
This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation via a fast 3D object gener-ation framework, named as GaussianDreamer, where the 3D diffusion model provides priors for initial-ization and the 2D diffusion model enriches the geometry and appearance.
- TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation337
Experimental results demonstrate that the proposed TopFormer method significantly outperforms CNN- and ViT-based networks across several semantic segmentation datasets and achieves a good trade-off between accuracy and latency.
- 291
- MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction290
This paper proposes a unified permutation-equivalent modeling approach, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
- Boundary-Preserving Mask R-CNN261
A conceptually simple yet effective Boundary-preserving Mask R-CNN (BMask R- CNN) to leverage object boundary information to improve mask localization accuracy in instance segmentation.
- Traffic sign detection and recognition using fully convolutional network guided proposals241
A novel framework with two deep learning components including fully convolutional network (FCN) guided traffic sign proposals and deep Convolutional neural network (CNN) for object classification to perform fast and accurate traffic sign detection and recognition.
- Automated defect inspection of LED chip using deep convolutional neural network239
The proposed CNN based defect inspector named LEDNet achieves impressively high performance on the inspection of LED chip defects with an inaccuracy of 5.04%, localizing exact defect regions as well.
- Occluded Video Instance Segmentation: A Benchmark229
A simple plug-and-play module that performs temporal feature calibration to complement missing object cues caused by occlusion is presented, and a remarkable AP improvement on the OVIS dataset is obtained.
- Sparse Instance Activation for Real-Time Instance Segmentation210
Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly out-performs the counterparts in terms of speed and accuracy.
- IoU-aware single-stage object detector for accurate localization206
IoU-aware single-stage object detector is proposed to solve the problem of low correlation between the classification score and localization accuracy of the predicted detections, which has severely hurt the localization accuracies of models.
- Weakly Supervised Region Proposal Network and Object Detection205
This paper proposes a weakly supervised region proposal network which is trained using only image-level annotations and achieves the state-of-the-art performance for WSOD with performance gain of about \(3\%\) on average.
- AlignSeg: Feature-Aligned Segmentation Networks199
This paper proposes Feature-Aligned Segmentation Networks (AlignSeg), a feature aggregation network that adopts a simple learnable interpolation strategy to learn transformation offsets of pixels that can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation.
- Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning194
The results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients and the method is extensible to image modalities such as MR imaging, CT and PET of other organs.
- DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks192
An accurate method for detecting the mitotic cells from histopathological slides using a novel multi‐stage deep learning framework and can achieve the highest F‐score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network.
- 190
- Deep Learning Representation using Autoencoder for 3D Shape Retrieval187
This work projects 3D shapes into 2D space and uses autoencoder for feature learning on the 2D images and shows the proposed deep learning feature is complementary to conventional local image descriptors, which can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.
- Bag of contour fragments for robust shape classification178
A new shape representation called Bag of Contour Fragments (BCF) inspired by classical Bag of Words (BoW) model is developed, which achieves the state-of-the-art performance on several well-known shape benchmarks, and can be applied to real image classification problem.
- ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities176
This work releases ONE-PEACE, a highly extensible model with 4B parameters that can seamlessly align and integrate representations across vision, audio, and language modalities, and develops two modality-agnostic pretraining tasks, which align the semantic space of different modalities and capture fine-grained details within modalities concurrently.
- Weakly supervised mitosis detection in breast histopathology images using concentric loss168
An automatic and accurate system for detecting mitosis in histopathology images using a deep segmentation network to produce segmentation map and a novel concentric loss function is proposed to train the semantic segmentsation network on weakly supervised mitosis data.
- SparseTrack: Multi-Object Tracking by Performing Scene Decomposition Based on Pseudo-Depth162
A pseudo-depth estimation method for obtaining the relative depth of targets from 2D images and a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets and perform data association on these sparse target subsets in order from near to far are proposed.
- 150
- TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance149
The method introduces two core techniques: affinity mimicking and weight inheritance, which enables student models to mimic teachers’ behavior of learning cross-modal feature alignment in a visual-linguistic affinity space during distillation.
- Densely Connected Search Space for More Flexible Neural Architecture Search138
This paper proposes to search block counts and block widths by designing a densely connected search space, i.e., DenseNAS, represented as a dense super network, which is built upon the designed routing blocks.
- 133
- Crossover Learning for Fast Online Video Instance Segmentation129
A novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames to enable efficient cross-frame instance-to-pixel relation learning and brings cost-free improvement during inference.
- PD-Quant: Post-Training Quantization Based on Prediction Difference Metric124
PD-Quant is proposed, a method that determines the quantization parameters by using the information of differences between network prediction before and after quantization, and improves the prediction accuracy of quantized models, especially in low-bit settings.
- A Simple Baseline for Multi-Object Tracking.122
This work studies the essential reasons behind the failure of object detection and re-identification, and presents a simple baseline that remarkably outperforms the state-of-the-arts on the public datasets at $30$ fps.
- IoU-Balanced loss functions for single-stage object detection120
Sufficient studies on MS COCO demonstrate that both IoU-balanced classification loss and Io U-balanced localization loss can bring substantial improvement for the single-stage detectors, and without whistles and bells, the proposed methods can improve AP by 1.1% for single- stage detectors.
- Max-Margin Multiple-Instance Dictionary Learning117
This paper designs a formulation to simultaneously learn mixtures of codes by maximizing classification margins in MIL, and results are observed in image classification benchmarks based on the learned codebooks, which observe both compactness and effectiveness.
- Object Detection in Videos by High Quality Object Linking113
This paper proposes a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets and improves results by 8.8 percent over the static image detector for fast moving objects.
- Multifunctional Environmental Smart Fertilizer Based on l -Aspartic Acid for Sustained Nutrient Release108
The as-prepared fertilizer has a promising application in sustainable modern agriculture and the water-holding and water-retention capacities of soil with the superabsorbent are obviously higher than those of the control soil without superabs absorbent.
- 104
- 101
- VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild95
VoxelTrack employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment and outperforms the state-of-the-art methods by a large margin on four public datasets.
- 94
- Relaxed Multiple-Instance SVM with Application to Object Discovery90
This paper proposes a novel method to solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM), which treats the positiveness of instance as a continuous variable, use Noisy-OR model to enforce the MIL constraints, and optimize them jointly in a unified framework.
- MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens89
This paper aims to alleviate the conflict between efficiency and flexibility, for which it proposes a specialized token for each region that serves as a messenger (MSG) that is integrated into a multi-scale architecture named MSG-Transformer.
- Temporally Efficient Vision Transformer for Video Instance Segmentation89
The proposed Temporally Efficient Vision Transformer (TeViT) is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head, which fully utilizes both frame-level and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost.
- MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images87
A robust and accurate technique for the automatic detection of mitoses from histological breast cancer slides using the multi-task deep learning framework for object detection and instance segmentation Mask RCNN, which outperforms all state-of-the-art mitosis detection approaches on the 2014 ICPR dataset.
- Deep attention network for joint hand gesture localization and recognition using static RGB-D images85
This paper presents an effective deep attention network for joint hand gesture localization and recognition using static RGB-D images and obtains the state-of-the-art hand gesture recognition performance on the challenging HUST-ASL dataset.
- 85
- Diversity Transfer Network for Few-Shot Learning84
A novel generative framework that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space, and obtains the state-of-the-art results among the feature generation based few-shot learning methods.
- 82
- BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation79
This work presents BoxTeacher, an efficient and end-to-end training framework for high-performance weakly supervised instance segmentation, which leverages a sophisticated teacher to generate high-quality masks as pseudo labels and proposes the noiseaware pixel loss and noise-reduced affinity loss to adaptively optimize the student with pseudo masks.
- 76
- Deep patch learning for weakly supervised object classification and discovery76
The results show that the proposed weakly supervised patch features via weak supervisions method can obtain state-of-the-art performance on object classification, and very competitive results on object discovery, with faster testing speed than competitors.
- Detect or Track: Towards Cost-Effective Video Object Detection/Tracking73
A scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers is proposed, which is more effective than the frame skipping baselines and flow-based approaches in video object detection/tracking.
- Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection71
The proposed detector, named MimDet, enables a MIM pre-trained vanilla ViT to outperform leading hierarchical architectures such as Swin Transformer, MViTv2 and ConvNeXt on COCO object detection & instance segmentation, and achieves better results compared with the previous best adapted vanilla ViT detector using a more modest fine-tuning recipe while converging 2.8× faster.
- DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention63
This paper aims to incorporate the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone, and introduces Diffusion Gated Linear Attention Transformers (DiG), a simple, adoptable solution with minimal parameter overhead.
- EfficientPose: Efficient human pose estimation with neural architecture search60
This paper proposes an efficient framework for human pose estimation with two parts, an efficient backbone and an efficient head, and customize the backbone network design for pose estimation, and reduces computational cost with negligible accuracy degradation.
- Robust Foreground Detection Using Smoothness and Arbitrariness Constraints59
This paper forms the problem in a unified framework from a probabilistic perspective, and design an effective algorithm to seek the optimal solution, and Experimental results on both synthetic and real data demonstrate the clear advantages of the method compared to the state of the art alternatives.
- WeakCLIP: Adapting CLIP for Weakly-Supervised Semantic Segmentation57
WeakCLIP provides an effective and parameter-efficient way to transfer CLIP knowledge to refine CAM and achieves the state-of-the-art WSSS performance on standard benchmarks.
- 57
- 55
- IgE Downregulates PTEN through MicroRNA-21-5p and Stimulates Airway Smooth Muscle Cell Remodeling50
In conclusion, non-immune IgE alone is sufficient for stimulated ASMC remodeling by upregulating microRNA-21-5p, and the suppression of micoRNAs may present a therapeutic target to reduce airway wall remodeling.
- Unsupervised local deep feature for image recognition50
In the experiments, ULDF achieves highly competitive performance comparing to the state-of-the-art image recognition methods, and the results suggest that the proposed local deep feature is superior than traditional global autoencoder.
- Human De-occlusion: Invisible Perception and Recovery for Humans48
A two-stage framework is proposed to estimate the invisible portions and recover the content inside of human deocclusion and a stacked network structure is devised to refine inaccurate masks from a general instance segmentation model and predict integrated masks simultaneously.
- 48
- Deep sketch feature for cross-domain image retrieval47
This paper shows that deep convolutional neural networks (DCNN) is also suitable for cross-domain image recognition, i.e., using sketch as query to retrieve natural images in a large dataset, and proposes to train CNN jointly using image data and sketch data in a novel way.
- 45
- Beyond diffusion process: Neighbor set similarity for fast re-ranking44
A simple yet effective method called Neighbor Set Similarity (NSS) is proposed, which is superior to both traditional pairwise similarity and diffusion process, and suggests that NSS is more suitable for large scale image retrieval than diffusion process.
- Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images43
The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation.
- 43
- 43
- 42
- 42
- 41
- 41
- FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search41
This paper proposes a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.
- 40
- 38
- 38
- Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection37
This paper first extracts the implicit latent state in mamba as capsule vectors, which abstract type-level capsules from pixel-level versions, which greatly reduce the computation and parameters caused by the pixel-level capsule routing for part-whole relationships exploration.
- 37
- Deep graph cut network for weakly-supervised semantic segmentation36
This paper proposes to use the graph cut algorithm to label more supervision from the activation seeds generated from a classification network to solve the weakly-supervised task achieving pixel-level semantic segmentation only with image-level labels as supervision.
- 36
- 36
- Structured random forest for label distribution learning35
An adaptive variable step method is proposed that can speed up the training process and reduce the calculations of information gain significantly and is suitable for the most decision tree based models.
- Neural features for pedestrian detection33
A pedestrian detection approach that uses neural features from a fully convolutional network (FCN) instead of features manually designed, which indicates that the proposed neural features are applicable to pedestrian detection task, due to their strong representation.
- Deep multi-metric learning for text-independent speaker verification32
Deep multi-metric learning is used to address the purpose of text-independent speaker verification and introduces three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss, which work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention.
- 31
- 30
- 29
- 29
- 28
- 28
- 28
- 27
- 27
- 27
- 26
- 26
- 26
- 26
- 25
- 25
- 24
- 24
- 24
- 24
- 23
- 22
- 21
- 21
- 21
- 21
- 21
- 21
- 20
- 20
- 20
- 19
- 19
- 19
- 18
- 18
- 18
- 17
- 17
- 17
- 17
- 17
- 17
- 16
- 16
- 16
- 16
- 15
- 15
- 15
- RILS: Masked Visual Reconstruction in Language Semantic Space14
A novel masked visual Reconstruction In Language semantic Space (RILS) pre-training framework, in which sentence representations serve as prototypes to transform the vision-only signals into patch-sentence probabilities as semantically meaningful MIM reconstruction targets.
- 14
- 14
- 13
- 13
- 13
- 13
- 13
- 13
- VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction12
VQRAE, a Vector Quantization version of Representation AutoEncoders, is proposed, which pioneers the first exploration in unified representation to produce Continuous semantic features for image understanding and Discrete tokens for visual generation within a unified tokenizer.
- Multiple Stage Residual Model for Accurate Image Classification12
This paper proposes an effective and efficient model, which is called Multiple Stage Residual Model (MSRM), to make full use of the residual vector to generate a multiple stage code.
- 12
- 11
- 11
- 11
- 11
- 11
- 10
- 10
- 10
- 10
- 10
- 10
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 6
- 6
- 6
- Deep Interactive Video Inpainting: An Invisibility Cloak for Harry Potter6
This is the first deep learning-based interactive video inpainting framework that only uses a free form of user input as guidance (i.e. scribbles) instead of mask annotations, which has academic, entertainment, and commercial value.
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.