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- 1,146
- Action2Motion677
This paper aims to generate plausible human motion sequences in 3D given a prescribed action type, and proposes a temporal Variational Auto-Encoder (VAE) that encourages a diverse sampling of the motion space.
- MoMask: Generative Masked Modeling of 3D Human Motions522
MoMask, a novel masked modeling framework for text-driven 3D human motion generation, outperforms the state-of-art methods on the text-to-motion generation task, with an FID of 0.045 and 0.228 on the HumanML3D dataset and KIT-ML, respectively.
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- Deep Learning for Visual Tracking: A Comprehensive Survey404
This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics, and extensively evaluates and analyzes the leading visualtracking methods.
- IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge386
The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
- 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.
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- Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications289
This work analyzes and discusses the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks, to give a comprehensive understanding of SAM’s practical applications.
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- Synthesizing retinal and neuronal images with generative adversarial nets211
Inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer, this approach enjoys several advantages and works well with a small training set with as few as 10 training examples, which is a common scenario in medical image analysis.
- Recognizing Complex Activities by a Probabilistic Interval-Based Model204
This work defines an atomic activity-based probabilistic framework that employs Allen's interval relations to represent local temporal dependencies and introduces a latent variable from the Chinese Restaurant Process to explicitly characterize these unique internal configurations of a particular complex activity as a variable number of tables.
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- Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models148
This paper proposes a discriminative semi-Markov model approach, and defines a set of features over boundary frames, segments, as well as neighboring segments that enable it to conveniently capture a combination of local and global features that best represent each specific action type.
- Integrated physiologic, proteomic, and metabolomic analyses of Malus halliana adaptation to saline–alkali stress134
This study provides an important starting point for improving saline–alkali tolerance in M. halliana via genetic engineering by analyzing the plant pathways involved in the stress response of the plant and its regulatory mechanisms.
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- Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups125
A unified paradigm based on Lie group theory is proposed, which enables us to collectively address pose estimation, tracking, and action recognition of articulated objects from depth images, and it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial baselines including convolutional neural networks and regression forest methods.
- Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis116
A supervised learning pipeline is addressed by the construction of a synthetic fundus image data set using the proposed R-sGAN technique, which helps to bridge the mismatch between the query images and the existing well-annotated data set.
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- Anterior Segment Optical Coherence Tomography Parameters in Subtypes of Primary Angle Closure99
Eyes with APAC had the narrowest angles, smallest anterior segment dimensions, thickest iris, and largest LV compared with PACS, PAC, and PACG.
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- Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction96
A novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context is proposed and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions.
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- Too Far to See? Not Really!—Pedestrian Detection With Scale-Aware Localization Policy88
An active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image and is demonstrated to produce overall lower detection errors on widely used benchmarks, and it works particularly well with far-scale pedestrians.
- Implicit Online Learning with Kernels87
A new, implicit update technique that can be applied to a wide variety of convex loss functions and a bounded memory version, SILK, that maintains a compact representation of the predictor without compromising solution quality, even in non-stationary environments are introduced.
- A novel imaging method for quantitative Golgi localization reveals differential intra-Golgi trafficking of secretory cargoes80
A novel imaging-based method is introduced to quantitatively localize Golgi proteins at nanometer resolution, which reveals different intra-Golgi trafficking of secretory cargoes.
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- EventHPE: Event-based 3D Human Pose and Shape Estimation79
A novel flow coherence loss is introduced by exploiting the fact that both flows are originated from the identical human motion, to mitigate the discrepancy between image-based flow (optical flow) and shape- based flow (vertices movement of human body shape).
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- Learning to compress images and videos71
This work presents an intuitive scheme for lossy color-image compression, using the color information from a few representative pixels to learn a model which predicts color on the rest of the pixels, and a simple active learning strategy to choose the representative pixels.
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- Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification64
In MSR-3DCNN, the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension is expanded, and 3D convolution and residual connection are combined to better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands.
- Myopia in Asian Subjects with Primary Angle Closure63
With the increasing rate of myopia in many East Asian populations, there may be many subjects with axial myopia but shallow ACD and angle closure, and ophthalmologists should not assume that glaucoma patients who are myopic have open angles.
- Subgrouping of Primary Angle-Closure Suspects Based on Anterior Segment Optical Coherence Tomography Parameters61
Clustering analysis identified 3 distinct subgroups of PACS subjects based on anterior segment optical coherence tomography and biometric parameters that may be relevant for understanding angle-closure pathogenesis and management.
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- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets52
This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.
- Improving retinal vessel segmentation with joint local loss by matting50
This work proposes a new approach as a post-processing step to improve existing methods by formulating segmentation as a matting problem, and is shown to be particularly effective in rescuing thin and tiny vessels that may lead to disconnections of vessel fragments.
- 3D Human Shape Reconstruction from a Polarization Image49
Empirical evaluations on a synthetic dataset as well as a real-world dataset demonstrate the qualitative and quantitative performance of the approach in estimating human poses and shapes indicates polarization camera is a promising alternative to the more conventional color or depth imaging for human shape estimation.
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- Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration45
An image analysis toolkit is developed to describe and characterize various modes of coordinated cell movements accompanying collective cell migration in Drosophilaborder cells and shows that individual cells may exchange positions during migration.
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- Delving into Calibrated Depth for Accurate RGB-D Salient Object Detection39
A novel framework that contains a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance and a boundary-aware multimodal fusion module to fuse the complementary cues from RGB and depth channels, as well as to improve object boundary qualities is proposed.
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- SemanticRT: A Large-Scale Dataset and Method for Robust Semantic Segmentation in Multispectral Images36
A SemanticRT dataset is introduced - the largest MSS dataset to date, comprising 11,371 high-quality, pixel-level annotated RGB-thermal image pairs, which is 7 times larger than the existing MFNet dataset.
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- Human Pose and Shape Estimation From Single Polarization Images34
A dedicated two-stage pipeline is proposed: given a single polarization image, stage one focuses on the fine detailed human body surface normal estimation; stage two then reconstructs clothed human shape from the polarization image and the estimated surface normal.
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- Multiview and Multimodal Pervasive Indoor Localization32
This paper addresses the deficiency of using a single measurement by proposing the original idea of localization based on multi-view and multi-modal measurements, and constructs a benchmark dataset for the PIL task, which to this knowledge is the first public dataset that is tailored for multi-View multi- modal indoor localization and contains both magnetic and visual signals.
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- Outlier Detection Ensemble with Embedded Feature Selection28
Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS, which unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier Detection method.
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- Structured learning of local features for human action classification and localization28
This work introduces a novel feature selection process called Sparse Hierarchical Bayes Filter and presents the application of structured learning in human action analysis, and proposes and shows that, by taking into account global configuration of local features, it can greatly improve recognition performance.
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- CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design21
A novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene, using an autoregressive model to sequentially arrange cuboids, producing physically plausible scenes.
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- Highly Efficient 3D Human Pose Tracking From Events With Spiking Spatiotemporal Transformer17
This work introduces the first sparse Spiking Neural Networks (SNNs) framework for 3D human pose tracking based solely on events, and eliminates the need to convert sparse data to dense formats or incorporate additional images, thereby fully exploiting the innate sparsity of input events.
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- Automated image based prominent nucleoli detection14
This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings and performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones.
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- Integrating local action elements for action analysis13
This paper proposes a framework for human action analysis from video footage that marginally outperforms all existing state-of-the-arts techniques on a range of different datasets and considers two different recipes to construct action elements.
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- Dual Learning Music Composition and Dance Choreography11
A novel extension of generative models for dance sequences conditioned on music by introducing an optimal transport objective to align feature embeddings, as well as a cycle consistency loss to foster overall consistency.
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- Multi-Modal Multi-Task Learning for Automatic Dietary Assessment10
An end-to-end neural model is proposed for automatic dietary assessment that outperforms two strong image captioning baselines significantly and introduces a novel memory network architecture to store meal representations and reason over the meal representations to support predictions.
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- Unleashing Multispectral Video's Potential in Semantic Segmentation: A Semi-supervised Viewpoint and New UAV-View Benchmark9
The introduction of MVUAV, a new MVSS benchmark dataset, and the development of a dedicated semi-supervised MVSS baseline - SemiMV are presented, proposed to enforce consistency regularization through a dedicated Cross-collaborative Consistency Learning (C3L) module and a denoised temporal aggregation strategy.
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- Music-to-Dance Generation with Optimal Transport7
A Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographies from music that gives a well defined and non-divergent training objective that mitigates the limitation of standard GAN training which is frequently plagued with instability and divergent generator loss issues.
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- Study on Transportation Network Monitoring based on High Resolution Aerial Images for City\|clusters–
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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.
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