Search Results for author:
Found 9 papers, 8 papers with code
SP$^2$OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering
To be more precise, we employ the strategy of majorization to reformulate the SP$^2$OT problem into a Progressive Partial Optimal Transport problem, which can be transformed into an unbalanced optimal transport problem with augmented constraints and can be solved efficiently by a fast matrix scaling algorithm.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches.
Novel Class Discovery for Long-tailed Recognition
In this paper, we consider a more realistic setting for novel class discovery where the distributions of novel and known classes are long-tailed.
Class-relation Knowledge Distillation for Novel Class Discovery
In addition, to enable a flexible knowledge distillation scheme for each data point in novel classes, we develop a learnable weighting function for the regularization, which adaptively promotes knowledge transfer based on the semantic similarity between the novel and known classes.
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities exist in input images.
Mutual Information-guided Knowledge Transfer for Novel Class Discovery
Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge.
Cascaded Sparse Feature Propagation Network for Interactive Segmentation
We aim to tackle the problem of point-based interactive segmentation, in which the key challenge is to propagate the user-provided annotations to unlabeled regions efficiently.
Ranked #4 on
Interactive Segmentation
on SBD
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods.
