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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.
- 464
- 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.
- 299
- 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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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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