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Found 12 papers, 11 papers with code
Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discovery
Despite their progress, our analysis experiments show that novel classes can achieve impressive clustering results on the feature space of a known class pre-trained model, suggesting that existing methods may not fully utilize known class knowledge.
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation
We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes.
CIBench: Evaluating Your LLMs with a Code Interpreter Plugin
Our evaluation framework includes an evaluation dataset and two evaluation modes.
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.
