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Found 18 papers, 8 papers with code
Towards Deconfounded Image-Text Matching with Causal Inference
Prior image-text matching methods have shown remarkable performance on many benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra-modal and inter-modal, and tend to learn the spurious correlations that extremely degrade the generalization ability of the model.
BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training
Recent methods model virtual try-on as image mask-inpaint task, which requires masking the person image and results in significant loss of spatial information.
The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges
In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.
Better Fit: Accommodate Variations in Clothing Types for Virtual Try-on
Image-based virtual try-on aims to transfer target in-shop clothing to a dressed model image, the objectives of which are totally taking off original clothing while preserving the contents outside of the try-on area, naturally wearing target clothing and correctly inpainting the gap between target clothing and original clothing.
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on, but have not resolved problems such as unnatural deformation of garments and the blurry generation quality.
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Consequently, this paper aims to improve the confidence with view selection and hierarchical prompts.
Image-Based Virtual Try-On: A Survey
Image-based virtual try-on aims to synthesize a naturally dressed person image with a clothing image, which revolutionizes online shopping and inspires related topics within image generation, showing both research significance and commercial potential.
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options.
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
The chest X-ray is often utilized for diagnosing common thoracic diseases.
Instrumental Variable Learning for Chest X-ray Classification
The chest X-ray (CXR) is commonly employed to diagnose thoracic illnesses, but the challenge of achieving accurate automatic diagnosis through this method persists due to the complex relationship between pathology.
Chest X-ray Image Classification: A Causal Perspective
The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest.
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities.
StyleIPSB: Identity-Preserving Semantic Basis of StyleGAN for High Fidelity Face Swapping
StyleIPSB gives us a novel tool for high-fidelity face swapping, and we propose a three-stage framework for face swapping with StyleIPSB.
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation
This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.
CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images
Several state-of-the-art segmentation methods are evaluated on this dataset.
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
To narrow this gap, we propose a network fusion architecture, which consists of a multispectral proposal network to generate pedestrian proposals, and a subsequent multispectral classification network to distinguish pedestrian instances from hard negatives.
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions.
Automated Generation of Geometric Theorems from Images of Diagrams
We propose an approach to generate geometric theorems from electronic images of diagrams automatically.
