Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- David W. JacobsSuggested from co-authorship
- Kazunori OkadaSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works465 from public data
- 2,959
- LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking1,655
LaSOT is presented, a high-quality benchmark for Large-scale Single Object Tracking that consists of 1,400 sequences with more than 3.5M frames in total, and is the largest, to the best of the authors' knowledge, densely annotated tracking benchmark.
- Detection and Tracking Meet Drones Challenge1,344
This work provides a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and concludes the challenge as well as propose future directions.
- Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection1,225
A novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), is proposed, Inspired by recent advances of deep learning in computer vision, for pavement crack detection that outperforms the state-of-the-art crack detection, edge detection, and semantic segmentation methods.
- 1,174
- 950
- 912
- M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network874
A powerful end-to-end one-stage object detector called M2Det is designed and train by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one- stage detectors.
- Salient Object Detection in the Deep Learning Era: An In-Depth Survey810
This paper reviews deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection, and looks into the generalization and difficulty of existing SOD datasets.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- 779
- 724
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection538
This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
- Robust Visual Tracking using 1 Minimization524
This paper proposes a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework and introduces a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure.
- 521
- 500
- Automatic thumbnail cropping and its effectiveness485
This work studies the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable, and evaluates automatic cropping techniques based on a general method that detects salient portions of images.
- Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking457
A multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in aSiamese network, which achieves state-of-the-art results and runs in real-time.
- Transfer Learning Based Visual Tracking with Gaussian Processes Regression454
This paper directly analyze this probability of target appearance as exponentially related to the confidence of a classifier output using Gaussian Processes Regression (GPR), and introduces a latent variable to assist the tracking decision.
- Clustered Object Detection in Aerial Images437
This paper proposes a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework and achieves promising performance in comparison with state-of-the-art detectors.
- LaSOT: A High-quality Large-scale Single Object Tracking Benchmark424
The goal in releasing LaSOT is to provide a dedicated high quality platform for both training and evaluation of trackers, and to take advantage of the close connection between visual appearance and natural language, the largest densely annotated tracking benchmark to be known.
- Human-Aware Motion Deblurring403
A human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG) humans based on a triple-branch encoder-decoder architecture that performs favorably against the state-of-the-art motion deblurred methods, especially in capturing semantic details.
- 378
- 371
- AutoFormer: Searching Transformers for Visual Recognition366
This work proposes a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search, which surpass the recent state-of-the-arts such as ViT and DeiT and achieves top-1 accuracy on ImageNet.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- 348
- 341
- 329
- Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking329
In the extensive experiments on popular benchmarks including OTB2013, OTB2015, TC128 and UAV20L, PTAV achieves the best tracking accuracy among all real-time trackers, and in fact performs even better than many deep learning based solutions.
- 328
- 322
- 313
- Feature Space Augmentation for Long-Tailed Data295
This work presents a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples, and decomposes the features of each class into a class-generic component and aclass-specific component using class activation maps.
- 279
- TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking269
This paper proposes TransMOT, which leverages powerful graph transformers to efficiently model the spatial and temporal interactions among the objects and achieves state-of-the-art performance on all the datasets.
- CBNet: A Novel Composite Backbone Network Architecture for Object Detection268
This paper proposes a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones, to form a more powerful backbone named Composite Backbone Network (CBNet), which can be very easily integrated into most state-of-the-art detectors and significantly improve their performances.
- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results259
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
- 257
- SANet: Structure-Aware Network for Visual Tracking256
This work utilizes recurrent neural network (RNN) to model object structure, and incorporates it into CNN to improve its robustness to similar distractors and shows that the proposed algorithm outperforms other methods.
- EDTER: Edge Detection with Transformer253
A novel transformer-based edge detector, Edge Detection TransformER (EDTER), to extract clear and crisp object boundaries and meaningful edges by exploiting the full image context information and detailed local cues simultaneously.
- FAMNet: Joint Learning of Feature, Affinity and Multi-Dimensional Assignment for Online Multiple Object Tracking240
An end-to-end model, named FAMNet, where Feature extraction, Affinity estimation and Multi-dimensional assignment are refined in a single network, where all layers in FAMNet are designed differentiable thus can be optimized jointly to learn the discriminative features and higher-order affinity model for robust MOT.
- 236
- CBNet: A Composite Backbone Network Architecture for Object Detection232
Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNet introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks.
- 221
- MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval220
This paper exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable, so that the derived hash codes are more semantically meaningful for various challenging cross-modal retrieval tasks.
- 218
- Searching the World’s Herbaria: A System for Visual Identification of Plant Species216
A working computer vision system that aids in the identification of plant species by extracting the leaf shape and matches it to the shape of leaves of known species is described.
- Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images206
A novel adjacent context coordination network (ACCoNet) to explore the coordination of adjacent features in an encoder–decoder architecture for RSI-SOD, which outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU.
- 198
- 185
- 179
- 176
- 174
- Vision Meets Drones: Past, Present and Future173
The VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South, is described, being the largest such dataset ever published, and enables extensive evaluation and investigation of visual analysis algorithms on the drone platform.
- Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation172
This article proposes a novel lightweight ORSI-SOD solution, named CorrNet, which achieves competitive or even better performance compared with 26 state-of-the-art methods, and meanwhile enjoys the clear memory and run-time efficiency.
- 168
- Cross-Modal Weighting Network for RGB-D Salient Object Detection166
This paper proposes a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD, and designs a composite loss function that summarizes the errors between intermediate predictions and ground truth over different scales.
- 165
- 164
- 161
- 159
- 158
- StructVIO: Visual-Inertial Odometry With Structural Regularity of Man-Made Environments155
The proposed VIO approach adopts structural regularity in man-made environments by using Atlanta world model to describe such regularity, and outperforms existing visual-inertial systems in large-scale man- made environments.
- 143
- 140
- 140
- Safe Self-Refinement for Transformer-based Domain Adaptation138
This paper finds that the combination of vision transformer with simple adversarial adaptation surpasses best reported Convolutional Neural Network (CNN)-based results on the challenging DomainNet benchmark, showing its strong transferable feature representation.
- Privacy-Protective-GAN for Privacy Preserving Face De-Identification136
This paper proposes a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN (generative adversarial network) with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.
- Salient Object Detection in Optical Remote Sensing Images Driven by Transformer134
GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies, and employs a Direction-aware Shuffle Weighted Spatial Attention Module and its simplified version to enhance local interactions, and a Knowledge Transfer Module to further enhance cross-level contextual interactions.
- Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images133
A novel multi-content complementation network (MCCNet) is proposed to explore the complementarity of multiple content for RSI-SOD, based on the general encoder–decoder architecture, and contains a novel key component named MCCM, which bridges the encoder and the decoder.
- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
- 133
- 130
- Cascaded Human-Object Interaction Recognition130
This work introduces a cascade architecture for a multi-stage, coarse-to-fine HOI understanding, and makes the framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling.
- Paying Attention to Video Object Pattern Understanding128
For the first time, it is quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgments during dynamic, task-driven viewing.
- First steps toward an electronic field guide for plants127
An ongoing project to digitize information about plant specimens and make it available to botanists in the field first requires digital images and models, and then effective retrieval and mobile computing mechanisms for accessing this information.
- Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment125
This paper proposes an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models, and considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy.
- 121
- Balancing Deformability and Discriminability for Shape Matching121
This work presents the aspect shape context, which extends shape context-based descriptors and adaptively selects the "best" aspect weight for shape comparison, and shows that this framework naturally unifies existing important shape and image representations by adjusting the aspect weight and the embedding.
- 120
- 115
- 114
- Diagnosing deep learning models for high accuracy age estimation from a single image112
A deep multi-task age estimation model is built which obtains a MAE of 2.96 on the Morph II dataset and 5.75 on the WebFace dataset, both of which improve previous best results by a large margin.
- 108
- 106
- 105
- Recurrent Exposure Generation for Low-Light Face Detection95
The proposed method, named REGDet, is the first ‘detection-with-enhancement’ framework for low-light face detection and not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection.
- 95
- Shape guided contour grouping with particle filters94
A novel framework for contour based object detection and recognition is proposed, which is formulated as a joint contour fragment grouping and labeling problem, and shows that this framework yields not only accurate object detections but also localizations in real cluttered images.
- 92
- Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features92
This work designs a hybrid feature pool containing recently invented descriptors including the stroke width transform and Weber’s local descriptors, as well as classical local features including intensity values, Gabor responses and vesselness measurements, and treats pixel-level vessel segmentation as a discriminative classification problem.
- 92
- Transductive Zero-Shot Learning With a Self-Training Dictionary Approach91
A bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space is proposed.
- RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth88
A novel neural network solution, RINDNet, to jointly detect all four types of edges, taking into consideration the distinct attributes of each type of edges and the relationship between them is proposed.
- 87
- VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results87
The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.
- Age regression from faces using random forests87
The first application of the random forest for age regression is proposed, which offers the advantage of few parameters that are relatively easy to initialize and learns salient anthropometric quantities without a prior model.
- 86
- 85
- 85
- The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector84
This paper proposes a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations, and converts the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations.
- 84
- VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results81
A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
- 81
- 80
- 80
- 76
- 73
- 72
- 72
- OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection69
A novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy, and an efficient one-shot search method to find the optimal path aggregation architecture.
- 69
- 67
- 65
- 65
- 65
- Cyclic Differentiable Architecture Search62
This work proposes new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS, which enables the evolution of the architecture to fit the final evaluation network.
- 62
- 62
- Image-based action recognition using hint-enhanced deep neural networks61
A new action recognition method is proposed by implicitly integrating pose hints into the CNN framework and then transferring a CNN originally learned for object recognition as a base network to action recognition by training the base network jointly with inference of poses.
- GMOT-40: A Benchmark for Generic Multiple Object Tracking59
The first publicly available dense GMOT dataset is constructed, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories, and two tracking protocols are adopted to evaluate different characteristics of tracking algorithms.
- 58
- A Container-Based Elastic Cloud Architecture for Pseudo Real-Time Exploitation of Wide Area Motion Imagery (WAMI) Stream57
A divide-and-conquer strategy is adopted to overcome the challenge resulting from the large amount of dynamic data, which allows dynamic resource provisioning to meet the performance requirements.
- 55
- 55
- Planar Object Tracking in the Wild: A Benchmark53
A carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment containing eleven state-of-the-art algorithms evaluated on the benchmark using two evaluation metrics.
- Classifying dynamic textures via spatiotemporal fractal analysis53
A powerful tool called dynamic fractal analysis to DT description and classification is presented, which integrates rich description of DT with strong robustness to environmental changes.
- 52
- 52
- Multi-Level Contextual RNNs With Attention Model for Scene Labeling51
This work proposes a novel approach for scene labeling by multi-level contextual recurrent neural networks (RNNs) that encode three kinds of contextual cues, viz., local context, global context, and image topic context in structural RNNs to model long-range local and global dependencies in an image.
- 50
- 49
- 48
- 47
- 47
- Crowd scene understanding with coherent recurrent neural networks46
A Coherent Long Short Term Memory (cLSTM) network is presented to capture the nonlinear crowd dynamics by learning an informative representation of crowd motions, which facilitates the critical tasks in crowd scene analysis.
- 44
- Summary of methods in Wide-Area Motion Imagery (WAMI)44
This paper summarizes the published literature on WAMI results in an effort to organize the techniques, discuss the developments, and determine the state-of-the-art.
- 44
- 41
- 41
- 41
- 41
- 40
- Robust Domain Adaptive Object Detection With Unified Multi-Granularity Alignment38
A unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning and an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness.
- INSURE: An Information Theory iNspired diSentanglement and pURification modEl for Domain Generalization38
This paper proposes an Information theory iNspired diSentanglement and pURification modEl (INSURE) to explicitly disentangle the latent features to obtain sufficient and compact class-relevant feature for generalization to the unseen domain and empirically shows that domain-specific class-relevant features are beneficial for domain generalization.
- Pattern of life from WAMI objects tracking based on visual context-aware tracking and infusion network models38
A framework for extracting the pattern of life (POL) of targets from WAMI video is presented and a context-aware multi-target tracker (CAMT) is applied to track multiple targets and obtain the targets’ tracklets, traces, and the locations, from surveillance information extracted from the targets' long-term trajectories.
- A novel pixel neighborhood differential statistic feature for pedestrian and face detection37
The pixel neighborhood differential pattern is learned with both supervised and unsupervised learning methods, which allow discovering discriminative pixel differential patterns in local area and achieving state-of-the-art results.
- 36
- Parallel Tracking and Verifying36
In extensive experiments on OTB2015, TC128, UAV20L, and VOT2016, PTAV achieves top tracking accuracy among all real-time trackers, and in fact, even outperforms many deep learning-based algorithms.
- Block covariance based l1 tracker with a subtle template dictionary36
A block-division based covariance feature is incorporated into the sparse representation framework and a subtle template dictionary is constructed including a fixed template, a stable template and other variational templates to capture the appearance variations and prevent the model from drifting.
- Smart City Surveillance in Fog Computing35
This chapter introduces the motivations and definition of smart cities as well as the existing challenges, and investigates the feasibility of Fog Computing for real-time urban surveillance using speeding traffic detection as a case study.
- Predicting Image Memorability by Multi-view Adaptive Regression35
The issue of automatically estimating the memorability of images by proposing a novel multi-view adaptive regression (MAR) model, which provides an effective mapping of visual features to memorability scores by taking advantage of robust feature selection and multiple feature integration.
- Cross gender–age trabecular texture analysis in cone beam CT35
The results support the use of CBCT for advanced trabecular analysis, including osteoporosis screening tools in the jaws, and fractal features were found to be better than intensity features in separating different gender-age groups.
- Transparent Object Tracking Benchmark34
A novel tracker is introduced, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin and is expected to facilitate future research and application of transparent object tracking in both the academia and industry.
- Object Discovery From a Single Unlabeled Image by Mining Frequent Itemsets With Multi-Scale Features34
This work proposes a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs), and achieves competitive localization performance compared with the state-of-the-art methods.
- Hidden in Plain Sight: Exploring Privacy Risks of Mobile Augmented Reality Applications33
The privacy risks associated with the hidden operations threat are presented and a framework for application development and runtime permissions targeted specifically at preventing the execution of hidden operations is proposed.
- End-to-End Full Projector Compensation33
The first end-to-end differentiable solution to solve the two problems of full projector compensation and photometric compensation jointly is proposed, and shows clear advantages over prior art with promising compensation quality and meanwhile being practically convenient.
- TapLab: A Fast Framework for Semantic Video Segmentation Tapping Into Compressed-Domain Knowledge33
A simple and effective framework, dubbed TapLab, to tap into resources from the compressed domain, which significantly reduces redundant computations of the state-of-the-art fast semantic image segmentation models, running 3 to 10 times faster with controllable accuracy degradation.
- 33
- 32
- 31
- 31
- 31
- 30
- 30
- 30
- 30
- Consistency and Diversity Induced Human Motion Segmentation29
A novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm that factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information.
- 29
- 28
- 28
- 28
- 28
- 28
- BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision27
This work proposes BioDrone—the first bionic drone-based visual benchmark for SOT, and conducts a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method.
- 27
- 27
- 27
- 26
- 26
- 26
- 26
- 26
- 26
- 25
- 25
- 25
- 25
- 24
- 24
- 24
- 24
- 23
- 23
- 23
- 22
- 22
- 21
- 21
- 21
- 21
- 21
- 21
- 20
- 20
- 20
- 20
- 19
- 19
- 19
- 19
- 18
- 18
- 17
- 17
- 17
- 17
- 17
- 17
- 16
- 16
- 16
- 16
- 16
- 16
- 15
- 15
- 15
- 15
- 15
- 15
- 15
- 14
- 14
- 14
- 14
- 14
- 13
- 13
- 13
- 13
- 12
- 12
- 12
- 12
- 12
- 12
- 11
- 11
- 11
- 11
- 11
- 11
- 11
- 11
- 11
- 11
- 11
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 10
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- 8
- 8
- 8
- 8
- 8
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 5
- 5
- Free-Form Composition Networks for Egocentric Action Recognition5
A free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos is proposed.
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 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
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- Sparse Reconstruction of Optical Doppler Tomography with Alternative State Space Model and Attention–
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
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.