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Found 17 papers, 7 papers with code
Semi-Supervised Unconstrained Head Pose Estimation in the Wild
To alleviate it, we propose the first semi-supervised unconstrained head pose estimation method SemiUHPE, which can leverage abundant easily available unlabeled head images.
Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
This study proposes an integration framework that blends learning behavior analysis with ML algorithms to enhance the prediction accuracy of students' online learning performance.
DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization
With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain task changes to directly adapt the pre-trained source model to arbitrary target domains equipped with prior domain knowledge, and we name this task Adaptive Domain Generalization (ADG).
S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection
These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy.
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation
The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image.
VPUFormer: Visual Prompt Unified Transformer for Interactive Image Segmentation
Specifically, we design a Prompt-unified Encoder (PuE) by using Gaussian mapping to generate a unified one-dimensional vector for click, box, and scribble prompts, which well captures users' intentions as well as provides a denser representation of user prompts.
Epistemic Graph: A Plug-And-Play Module For Hybrid Representation Learning
In this paper, a novel Epistemic Graph Layer (EGLayer) is introduced to enable hybrid learning, enhancing the exchange of information between deep features and a structured knowledge graph.
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
To tackle this problem, this paper proposes a novel "Supervised Scale-aware Deformable Attention" (SSDA) for monocular 3D object detection.
OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset
The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation.
Learning to Learn Domain-invariant Parameters for Domain Generalization
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
A Survey of Visual Transformers
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
An automated and multi-parametric algorithm for objective analysis of meibography images
Meibography is a non-contact imaging technique used by ophthalmologists to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD).
Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images
Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.
