Search Results for author:
Found 8 papers, 2 papers with code
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Directly fine-tuning VLMs for RS-FGSC often encounters the challenge of overfitting the seen classes, resulting in suboptimal generalization to unseen classes, which highlights the difficulty in differentiating complex backgrounds and capturing distinct ship features.
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching
The lack of object-level annotations poses a significant challenge for object detection in remote sensing images (RSIs).
Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentation
Pseudo-labelling is a popular technique in unsuper-vised domain adaptation for semantic segmentation.
Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization
In this paper, we propose a deep protein subcellular localization method with multi-marginal contrastive learning to perceive the same PSLs in different tissue images and different PSLs within the same tissue image.
Exploring Representativeness and Informativeness for Active Learning
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Meanwhile, it is also hard to build a good model without diagnosing discriminative labels.
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach
Simultaneously, our proposed method makes full use of the label information, and the proposed active learning is designed based on multiple classes.
