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- Deep 3D human pose estimation: A review402
A thorough review of existing deep learning based works for 3D pose estimation is provided, the advantages and disadvantages of these methods are summarized, and the commonly-used benchmark datasets are explored for comparison and analysis.
- Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks366
This paper proposes a trellis encoder-decoder network (TEDnet) for crowd counting that achieves the best overall performance in terms of both density map quality and counting accuracy, and proposes a new combinatorial loss to enforce similarities in local coherence and spatial correlation between maps.
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- Few-Shot Semantic Segmentation with Democratic Attention Networks241
This paper introduces the de-mocratized graph attention mechanism, which can activate more pixels on the object to establish a robust correspondence between support and query images, and proposes multi-scale guidance by designing a refinement fusion unit to fuse features from intermediate layers for the segmentation of the query image.
- Spatio-Temporal Laplacian Pyramid Coding for Action Recognition236
The proposed STLPC method achieves superb recognition rates on the KTH, the multiview IXMAS, the challenging UCF Sports, and the newly released HMDB51 datasets, and outperforms state of the art methods showing its great potential on action recognition.
- Learning to Learn with Variational Information Bottleneck for Domain Generalization206
A probabilistic meta-learning model for domain generalization is introduced, in which classifier parameters shared across domains are modeled as distributions, which enables better handling of prediction uncertainty on unseen domains.
- Memory Attention Networks for Skeleton-Based Action Recognition204
A newly temporal-then-spatial recalibration method named memory attention networks (MANs) is proposed and deploy MANs using the temporal attention recalibrration module (TARM) and spatiotemporal convolution module (STCM) to handle skeleton-based action recognition.
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- Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation115
A general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and unreliable assumptions, and largely outperforms existing direct methods on a larger dataset of 100 subjects including both healthy and diseased cases with twice the number of subjects used in previous methods.
- Learning Discriminative Key Poses for Action Recognition99
A new classifier named weighted local naive Bayes nearest neighbor is proposed for the final action classification, which is demonstrated to be more accurate and robust than other classifiers, e.g., support vector machine (SVM) and naive Baye nearest neighbor.
- Conditional Variational Image Deraining91
A Conditional Variational Image Deraining (CVID) network is proposed for better deraining performance, leveraging the exclusive generative ability of Conditionalvariational Auto-Encoder on providing diverse predictions for the rainy image.
- Multi-Stream Convolutional Neural Network for SAR Automatic Target Recognition85
This paper proposes a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views that can achieve high recognition rates and outperform other state-of-the-art ATR methods.
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- Direct Estimation of Cardiac Bi-ventricular Volumes with Regression Forests77
With the proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.
- Transductive Relation-Propagation Network for Few-shot Learning76
This work proposes a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs, the first work that explicitly takes the relations of support- query pairs into consideration in few-shot learning.
- Embedding Motion and Structure Features for Action Recognition72
This work proposes a novel method to model human actions by explicitly coding motion and structure features that are separately extracted from video sequences and integrates them in a unified framework, which provides an informative and compact representation of human actions.
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- Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models63
This paper designs two complementary types of knowledge-aware prompts for the text encoder to leverage the distinctive characteristics of category-related external knowledge and designs an adaptation head for the visual encoder to aggregate salient attentive visual cues, which establishes discriminative and task-aware visual representations.
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- Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer58
This work investigated physics-informed deep neural networks for estimating physiological parameters from DCE-MRI signal-curves and selected the GRU framework for in vivo evaluation and compared to the spatio-temporal frameworks in 28 patients with pancreatic cancer.
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 2026-10-14. 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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