Search Results for author:
Found 17 papers, 11 papers with code
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis
In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, called PoCo, for ophthalmic disease diagnosis.
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini.
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
BlindDiff seamlessly integrates the MAP-based optimization into DMs, which constructs a joint distribution of the low-resolution (LR) observation, high-resolution (HR) data, and degradation kernels for the data and kernel priors, and solves the blind SR problem by unfolding MAP approach along with the reverse process.
AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey
With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
Ranked #1 on
Pedestrian Image Caption
on CUHK-PEDES
Assumption-lean and Data-adaptive Post-Prediction Inference
A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be both costly and labor-intensive to obtain.
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden.
Described Object Detection: Liberating Object Detection with Flexible Expressions
In this paper, we advance them to a more practical setting called Described Object Detection (DOD) by expanding category names to flexible language expressions for OVD and overcoming the limitation of REC only grounding the pre-existing object.
Advancing Referring Expression Segmentation Beyond Single Image
To overcome this limitation, we propose a more realistic and general setting, named Group-wise Referring Expression Segmentation (GRES), which expands RES to a collection of related images, allowing the described objects to be present in a subset of input images.
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models.
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function.
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model.
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation.
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.
