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Found 9 papers, 4 papers with code
Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning
Additionally, we demonstrate that our attention map-based grounding technique significantly outperforms direct localization predictions from MiniCPM-Llama3-V 2. 5, highlighting the potential of using attention maps from pretrained MLLMs and paving the way for future innovations in this domain.
Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors
The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts.
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlabeled data to build a predictive model.
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View
Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation.
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation
Knowledge transfer from multi-modal, i. e., LiDAR points and images, to a single LiDAR modal can take advantage of complimentary information from modal-fusion but keep a single modal inference speed, showing a promising direction for point cloud semantic segmentation in autonomous driving.
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones.
Dual Decision Improves Open-Set Panoptic Segmentation
Then we distinguish the "unknown things" from the background by using the additional object prediction head.
Progressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification.
Semi-supervised Learning via Conditional Rotation Angle Estimation
Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.
