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Found 18 papers, 10 papers with code
VG4D: Vision-Language Model Goes 4D Video Recognition
By transferring the knowledge of the VLM to the 4D encoder and combining the VLM, our VG4D achieves improved recognition performance.
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
Through extensive experiments, we demonstrate that our PointMLS achieves state-of-the-art results on ModelNet-O and competitive results on regular datasets, and it is robust and effective.
Explore Human Parsing Modality for Action Recognition
Multimodal-based action recognition methods have achieved high success using pose and RGB modality.
Ranked #4 on
Action Recognition
on NTU RGB+D 120
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN).
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Compared with the existing methods, GrowCLIP improves 2. 3% average top-1 accuracy on zero-shot image classification of 9 downstream tasks.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns.
Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
In MOTOR, we combine two kinds of basic medical knowledge, i. e., general and specific knowledge, in a complementary manner to boost the general pretraining process.
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
Ranked #3 on
Human Pose Forecasting
on Human3.6M
Cross-Modal Causal Intervention for Medical Report Generation
Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding medical reports according to the given radiology image.
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining
Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e. g., 15. 44% mAP on VG V1. 2 and 13. 98% on the VG-COCO dataset.
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
For the class-center involved triplet loss, the positive and negative samples in each triplet are replaced by their corresponding class centers, which enforces data representations of the same class closer to the class center.
Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism
To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.
Maximizing Unambiguous Velocity Range in Phase-contrast MRI with Multipoint Encoding
In phase-contrast magnetic resonance imaging (PC-MRI), the velocity of spins at a voxel is encoded in the image phase.
Venc Design and Velocity Estimation for Phase Contrast MRI
We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing.
High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRI
Purpose: To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.
Convolutional Framework for Accelerated Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation.
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm
Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.
