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Found 10 papers, 4 papers with code
Exploring the Privacy Protection Capabilities of Chinese Large Language Models
Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence.
Multi-Task Dense Prediction via Mixture of Low-Rank Experts
Furthermore, to control the parameters and computational cost brought by the increase in the number of experts, we take inspiration from LoRA and propose to leverage the low-rank format of a vanilla convolution in the expert network.
Empowering Segmentation Ability to Multi-modal Large Language Models
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability.
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Motivated by these, we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies.
Meta-DM: Applications of Diffusion Models on Few-Shot Learning
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies.
Few-Shot Learning
Unsupervised Few-Shot Image Classification
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem.
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision.
Traffic Scene Parsing through the TSP6K Dataset
To date, most existing datasets focus on autonomous driving scenes.
Multi-head Uncertainty Inference for Adversarial Attack Detection
We adopt a multi-head architecture with multiple prediction heads (i. e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI.
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation
Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks.
Ranked #16 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
