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Found 12 papers, 4 papers with code
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification
In the personalized branch, we integrate vectorized demographic information (i. e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics.
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting
After that, to capture the complex spatio-temporal correlations simultaneously in these vectors, we utilize Transformer encoder and stack it with several layers.
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps.
Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning
Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification.
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
Different from the typical transformer blocks, the relation aggregators in our UniFormer block are equipped with local and global token affinity respectively in shallow and deep layers, allowing to tackle both redundancy and dependency for efficient and effective representation learning.
Ranked #151 on
Image Classification
on ImageNet
Pose-guided Generative Adversarial Net for Novel View Action Synthesis
We focus on the problem of novel-view human action synthesis.
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos.
Ranked #10 on
3D Human Pose Estimation
on HumanEva-I
Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud
This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds.
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos
By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module.
Ranked #4 on
3D Human Pose Estimation
on Surreal
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.
Ranked #1 on
Point Cloud Segmentation
on PointCloud-C
Domain Ontology Learning Enhanced by Optimized Relation Instance in DBpedia
Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field.
