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
- Pascal FuaSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works358 from public data
- Context-Aware Crowd Counting735
This paper introduces an end-to-end trainable deep architecture that combines features obtained using multiple receptive field sizes and learns the importance of each such feature at each image location, which yields an algorithm that outperforms state-of-the-art crowd counting methods, especially when perspective effects are strong.
- Learning Trajectory Dependencies for Human Motion Prediction584
A simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints, and design a new graph convolutional network to learn graph connectivity automatically.
- Learning to Find Good Correspondences551
A novel normalization technique, called Context Normalization, is introduced, which allows the network to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences.
- Beyond Sharing Weights for Deep Domain Adaptation479
This work introduces a two-stream architecture, where one operates in the source domain and the other in the target domain, and demonstrates that this both yields higher accuracy than state-of-the-art methods on several object recognition and detection tasks and consistently outperforms networks with shared weights in both supervised and unsupervised settings.
- 463
- Evaluating The Search Phase of Neural Architecture Search383
This paper finds that on average, the state-of-the-art NAS algorithms perform similarly to the random policy; the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process.
- 382
- History Repeats Itself: Human Motion Prediction via Motion Attention381
An attention-based feed-forward network is introduced that explicitly leverages the observation that human motion tends to repeat itself to capture motion attention to capture the similarity between the current motion context and the historical motion sub-sequences.
- Segmentation-Driven 6D Object Pose Estimation329
This paper introduces a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations and uses a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences.
- Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices316
To encode the geometry of the manifold in the mapping, a family of provably positive definite kernels on the Riemannian manifold of SPD matrices is introduced, derived from the Gaussian kernel, but exploit different metrics on the manifold.
- Structured Prediction of 3D Human Pose with Deep Neural Networks282
This paper introduces a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and account for joint dependencies.
- Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels270
It is shown that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels.
- Learning Monocular 3D Human Pose Estimation from Multi-view Images265
This paper trains the system to predict the same pose in all views, and proposes a method to estimate camera pose jointly with human pose, which lets us utilize multiview footage where calibration is difficult, e.g., for pan-tilt or moving handheld cameras.
- Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation262
A novel architecture designed to deliver the best of both worlds by performing both simultaneously and fusing the information along the way and yields significant improvements upon the state-of-the-art on standard 3D human pose estimation benchmarks.
- Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation242
This paper proposes a geometry-aware body representation from multi-view images without annotations that significantly outperforms fully-supervised methods given the same amount of labeled data, and improves over other semi-super supervised methods while using as little as 1% of the labeled data.
- Detecting the Unexpected via Image Resynthesis235
This paper tackles the more realistic scenario where unexpected objects of unknown classes can appear at test time and translates the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions, which outperforms both uncertainty- and autoencoder-based methods.
- Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods228
This paper proposes to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection and shows that learning can be expressed as an optimization problem on a Grassmann manifold and discusses fast solutions for special cases.
- Single-Stage 6D Object Pose Estimation227
This work introduces a deep architecture that directly regresses 6D poses from correspondences and can be exploited in conjunction with existing correspondence-extraction networks so as to yield single-stage 6D pose estimation frameworks.
- From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices214
An approach that lets us handle high-dimensional SPD matrices by constructing a lower-dimensional, more discriminative SPD manifold is introduced and leads to a significant accuracy gain over state-of-the-art methods.
- Factorized Latent Spaces with Structured Sparsity206
This paper shows that structured sparsity allows us to address the multi-view learning problem by alternately solving two convex optimization problems and shows that the resulting factorized latent spaces generalize over existing approaches in that they allow having latent dimensions shared between any subset of the views instead of between all the views only.
- 204
- Encouraging LSTMs to Anticipate Actions Very Early184
A new action anticipation method that achieves high prediction accuracy even in the presence of a very small percentage of a video sequence, and develops a multi-stage LSTM architecture that leverages context-aware and action-aware features, and introduces a novel loss function that encourages the model to predict the correct class as early as possible.
- GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence179
GigaPose first leverages discriminative “templates ”, ren-dered images of the CAD models, to recover the out-of-plane rotation and then uses patch correspondences to estimate the four remaining parameters, resulting in a speedup factor of 35x compared to the state of the art.
- CLIP the Gap: A Single Domain Generalization Approach for Object Detection169
This work proposes to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts to address the challenges of simultaneously learning robust object localization and representation, and outperforms the only existing SDG object detection method, Single-DGOD.
- Effective Use of Synthetic Data for Urban Scene Semantic Segmentation164
A drastically different way to handle synthetic images that does not require seeing any real images at training time is introduced, which builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently.
- 164
- GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping162
This work builds upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features and fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions.
- 162
- 161
- Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation154
This work proposes a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules, and introduces a new form of inexpensive weak supervision yielding an additional accuracy boost.
- 153
- 146
- 145
- A Stochastic Conditioning Scheme for Diverse Human Motion Prediction142
This paper proposes to stochastically combine the root of variations with previous pose information, so as to force the model to take the noise into account, and exploits this idea for motion prediction by incorporating it into a recurrent encoder-decoder network with a conditional variational autoencoder block that learns to exploit the perturbations.
- Factorized Orthogonal Latent Spaces130
This paper proposes a robust approach to factorizing the latent space into shared and private spaces by introducing orthogonality constraints, which penalize redundant latent representations.
- Wide-Depth-Range 6D Object Pose Estimation in Space129
This work proposes a single-stage hierarchical end-to-end trainable network that outperforms existing approaches not only on images synthesized to resemble images taken in space but also on standard benchmarks.
- 129
- Closed-Form Solution to Non-rigid 3D Surface Registration128
This work presents a closed-form solution to the problem of recovering the 3D shape of a non-rigid inelastic surface from 3D-to-2D correspondences that lets us detect and reconstruct such a surface by matching individual images against a reference configuration, in contrast to all existing approaches that require initial shape estimates and track deformations from image to image.
- 126
- Recurrent U-Net for Resource-Constrained Segmentation124
This paper introduces a novel recurrent U-Net architecture that preserves the compactness of the original U- net, while substantially increasing its performance to the point where it outperforms the state of the art on several benchmarks.
- 120
- A Family of MCMC Methods on Implicitly Defined Manifolds120
A general constrained version of Hamiltonian Monte Carlo is proposed, and conditions under which the Markov chain is convergent are given, which define a family of MCMC methods which can be applied to sample from distributions on non-linear manifolds.
- Distribution-matching embedding for visual domain adaptation119
This paper introduces a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized.
- 116
- Bregman Divergences for Infinite Dimensional Covariance Matrices114
This work proposes an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs.
- Generating Smooth Pose Sequences for Diverse Human Motion Prediction113
This paper designs a generator that predicts the motion of different body parts sequentially, and introduces a normalizing flow based pose prior, together with a joint angle loss, to achieve motion realism.
- Progressive Correspondence Pruning by Consensus Learning112
This work introduces a "pruning" block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs and achieves progressive pruning by stacking multiple pruning blocks sequentially.
- 109
- Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions106
A method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions and is the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets, and outperform the state-of-the-art template matching methods on the T-LESS datasets.
- Multi-level Motion Attention for Human Motion Prediction101
This work proposes to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences to effectively exploit motion patterns from the long-term history to predict the future poses.
- 101
- Expanding the Family of Grassmannian Kernels: An Embedding Perspective101
This work introduces several positive definite Grassmannian kernels, including universal ones, and demonstrates their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing.
- MuIT: An End-to-End Multitask Learning Transformer100
An end-to-end Multitask Learning Transformer framework to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection is proposed.
- Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking99
A probabilistic autoregressive motion model is introduced to score tracklet proposals by directly measuring their likelihood, achieved by training the model to learn the underlying distribution of natural tracklets.
- 98
- 91
- Deep Convolutional Neural Networks for Human Embryonic Cell Counting86
The results show that the proposed framework provides robust estimates of the number of cells in a developing embryo up to the 5-cell stage (i.e., 48 h post fertilization).
- 86
- Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data77
This paper revisits the Shape Interaction Matrix and reveals its connections to several recent subspace clustering methods, and derives a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise.
- LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields76
The extensive experiments on the scene-level KITTI-360 dataset, and on the object-level NeRF-MVL show that the LiDAR-NeRF surpasses the model-based algorithms significantly, and a structural regularization method to preserve local structural details is introduced.
- 75
- Learning Latent Representations of 3D Human Pose with Deep Neural Networks75
This paper introduces a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images or 2D joint location heatmaps that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and accounts for joint dependencies and proposes an efficient Long Short-Term Memory network to enforce temporal consistency on 3D pose predictions.
- Geometry-Aware Deep Network for Single-Image Novel View Synthesis71
A new region-aware geometric transform network is developed that performs these multiple tasks in a common framework and demonstrates the effectiveness of the network in generating high-quality synthetic views that respect the scene geometry, thus outperforming the state-of-the-art methods.
- Robust Outlier Rejection for 3D Registration with Variational Bayes70
A novel variational non-local network-based outlier rejection framework for robust alignment and a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation are proposed.
- Joint dimensionality reduction and metric learning: a geometric take69
A Riemannian framework is developed to jointly learn a mapping performing dimensionality reduction and a metric in the induced space and it is shown that this approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.
- 68
- Shape Reconstruction by Learning Differentiable Surface Representations67
This paper shows that the inherent differentiability of deep networks can be exploited to leverage differential surface properties during training so as to prevent patch collapse and strongly reduce patch overlap, and this lets us reliably compute quantities such as surface normals and curvatures.
- DrapeNet: Garment Generation and Self-Supervised Draping66
This work relies on self-supervision to train a single network to drape multiple garments by predicting a 3D deformation field conditioned on the latent codes of a generative network, which models garments as unsigned distance fields.
- 66
- Robust Differentiable SVD65
It is shown that the Taylor expansion of the SVD gradient is theoretically equivalent to the gradient obtained using PI without relying in practice on an iterative process and thus yields more accurate gradients, which results in increased accuracy for image classification and style transfer.
- Capturing 3D stretchable surfaces from single images in closed form63
It is shown that replacing the inextensibility constraints by shading ones removes this limitation while still allowing 3D reconstruction in closed-form, and can reconstruct a surface from a single image without a priori knowledge of its deformations in that image.
- 62
- A domain-adaptive two-stream U-Net for electron microscopy image segmentation61
A Domain Adaptation approach that relies on two coupled U-Nets that either regularize or share corresponding weights between the two streams, along with a differentiable loss function that approximates the Jaccard index, to leverage training data from one domain in which it is plentiful, to adapt the network weights in another where it is scarce.
- Detecting Road Obstacles by Erasing Them59
This work selects image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches, and uses a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased obstacle.
- Residual Parameter Transfer for Deep Domain Adaptation59
This work introduces a network architecture that includes auxiliary residual networks, which is able to flexibly preserve the similarities between domains where they exist and model the differences when necessary, and demonstrates that this approach yields higher accuracy than state-of-the-art methods without undue complexity.
- Fast Adversarial Training With Adaptive Step Size58
It is shown that catastrophic overfitting is instance-dependent, and fitting instances with larger input gradient norm is more likely to cause catastrophic over fitting, and a simple but effective method, Adversarial Training with Adaptive Step size (ATAS), is proposed, which learns an instance-wise adaptive step size that is inversely proportional to its gradient norm.
- GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss57
A two-stream deep network model is introduced that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes and achieves superior performance against a recently proposed data-driven method.
- Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image Classification57
A geometry-aware deep recurrent neural network (Geo-DRNN) for HSI classification that outperforms the state-of-the-art classification methods and can learn complex geometries is proposed.
- 56
- VIENA $$^2$$ : A Driving Anticipation Dataset53
This paper introduces a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.
- Non-associative Higher-Order Markov Networks for Point Cloud Classification53
This paper proposes a method to extract informative cliques in 3D point clouds that provide more knowledge about the context of the scene and evidence the benefits of the non-associative higher-order Markov networks over state-of-the-art point cloud labeling techniques.
- Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation53
This paper shows that the mean prediction of a Gaussian process implicitly satisfies linear constraints if those constraints are satisfied by the training examples, and shows how, by performing a change of variables, a GP can be forced to satisfy quadratic constraints.
- Adversarial Parametric Pose Prior51
This work proposes learning a prior that restricts the SMPL parameters to values that produce realistic poses via adversarial training, and shows that the learned prior outperforms the state-of-the-art VAE-based approach to constraining theSMPL parameters.
- Learning to Reconstruct Texture-Less Deformable Surfaces from a Single View50
This paper introduces a general framework that can predict diverse 3D representations, such as meshes, normals, and depth maps, and demonstrates that this approach generalizes well to unseen objects, and yields higher-quality reconstructions than a state-of-the-art SfS technique, particularly in terms of normal estimates.
- Eigendecomposition-Free Training of Deep Networks with Zero Eigenvalue-Based Losses50
This paper introduces an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network.
- Neural Scene Decomposition for Multi-Person Motion Capture49
A self-supervised approach to learning what is called a neural scene decomposition (NSD), which encodes 3D geometry and can be effectively leveraged to train a 3D pose estimation network from small amounts of annotated data.
- Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs49
This work proposes to go beyond state-of-the-art image-set matching techniques and model image-sets as probability distribution functions (PDFs) using kernel density estimators and introduces valid positive definite kernels on the statistical manifolds.
- Statistically-Motivated Second-Order Pooling48
This work introduces a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models.
- Motion Prediction Using Temporal Inception Module47
A Temporal Inception Module (TIM) to encode human motion is proposed, making use of TIM, and the framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths.
- 46
- 46
- Estimating People Flows to Better Count Them in Crowded Scenes45
This paper advocates estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing, which enables us to impose much stronger constraints encoding the conservation of the number of people.
- 45
- 44
- Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction42
The task of action-driven stochastic human motion prediction is introduced, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history, and a VAE-based model conditioned on both the observed motion and the action label sequence is designed.
- 42
- TempSAL - Uncovering Temporal Information for Deep Saliency Prediction41
This work introduces a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns and locally modulates the saliency predictions by combining the learned temporal maps.
- Counting People by Estimating People Flows41
This paper advocates estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them, which enables us to impose much stronger constraints encoding the conservation of the number of people.
- Bringing Background into the Foreground: Making All Classes Equal in Weakly-Supervised Video Semantic Segmentation41
This paper introduces an approach to weak supervision using only image tags by making use of classifier heatmaps, and develops a two-stream deep architecture that jointly leverages appearance and motion, and design a loss based on the authors' heatmaps to train it.
- Optimizing over Radial Kernels on Compact Manifolds41
It is shown that positive definite radial kernels on the unit n-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework.
- Robust Motion Segmentation with Unknown Correspondences41
This paper introduces an approach to performing motion segmentation without any prior knowledge of point correspondences in terms of Partial Permutation Matrices (PPMs) and aims to match feature descriptors while simultaneously encouraging point trajectories to satisfy subspace constraints.
- Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation40
This work proposes a novel method to extract accurate masks from networks pre-trained for the task of object recognition, thus forgoing external objectness modules, and shows how foreground/background masks can be obtained from the activations of higher-level convolutional layers of a network.
- 39
- 39
- 38
- 35
- A Framework for Shape Analysis via Hilbert Space Embedding35
This work proposes to map shapes on Kendall's shape manifold to a high dimensional Hilbert space where Euclidean geometry applies, and introduces a kernel on this manifold that permits such a mapping, and proves its positive definiteness.
- 33
- 33
- 30
- 30
- 30
- 30
- 27
- 27
- 27
- 26
- 26
- 26
- 26
- 25
- 25
- 24
- 24
- 24
- 24
- 23
- 22
- 22
- 22
- 22
- 22
- 21
- 21
- 21
- 21
- 20
- 20
- 20
- 19
- 19
- 18
- 18
- 18
- Automated monitoring of human embryonic cells up to the 5-cell stage in time-lapse microscopy images18
- 18
- 17
- 17
- 17
- 17
- 16
- 16
- Efficient Linear Programming for Dense CRFs16
This work develops a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent, and shows that each block of variables can be efficiently optimized.
- Robust Multi-Body Feature Tracker: A Segmentation-Free Approach16
A segmentationfree solution to multi-body feature tracking that bypasses the motion assignment step and reduces to solving a series of subproblems with closed-form solutions is introduced.
- 16
- 15
- 15
- 15
- 15
- 15
- 14
- 14
- 14
- 14
- 13
- 13
- Low-Rank Kernel Subspace Clustering.13
This paper proposes to learn a low-rank kernel mapping, with which the mapped data in feature space are not only low- rank but also self-expressive, such that the low-dimensional subspace structures are present and manifested in the high-dimensional feature space.
- 13
- 12
- 12
- 12
- 12
- 12
- 12
- 12
- 11
- 11
- 11
- 11
- 10
- 10
- 10
- Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials10
It is shown that a dense CRF can be aggregated with sparse higher order potentials in a way that is amenable to continuous relaxations and solved a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework.
- 10
- 10
- 10
- 9
- 9
- 9
- 9
- 9
- 9
- 8
- 8
- 8
- 8
- 8
- 8
- 7
- 7
- 7
- 7
- 7
- 7
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 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
- Non-Linear Subspace Clustering with Learned Low-Rank Kernels1
This paper proposes to learn a low-rank kernel matrix, with which mapped data in feature space are not only low- rank but also self-expressive, and the low-dimensional subspace structures of the (implicitly) mapped data are retained and manifested in the high-dimensional feature space.
- 1
- 1
- When VLAD Met Hilbert1
This paper addresses two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality and proposes an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VL AD.
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds–
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
Publication data from OpenAlex; 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.