2023-01-13 更新
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
Authors:Parisa Jamadi Khiabani, Arkaitz Zubiaga
Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance towards new targets for which the model has only seen few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN towards the final performance. We further analyse the network interactions between social media users, which reveal the potential of using social features for cross-target stance detection.
PDF
点此查看论文截图
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Authors:Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang
Contrastive language-image pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D tasks, applying CLIP to help the learning in 3D scene understanding has yet to be explored. In this paper, we make the first attempt to investigate how CLIP knowledge benefits 3D scene understanding. To this end, we propose CLIP2Scene, a simple yet effective framework that transfers CLIP knowledge from 2D image-text pre-trained models to a 3D point cloud network. We show that the pre-trained 3D network yields impressive performance on various downstream tasks, i.e., annotation-free and fine-tuning with labelled data for semantic segmentation. Specifically, built upon CLIP, we design a Semantic-driven Cross-modal Contrastive Learning framework that pre-trains a 3D network via semantic and spatial-temporal consistency regularization. For semantic consistency regularization, we first leverage CLIP’s text semantics to select the positive and negative point samples and then employ the contrastive loss to train the 3D network. In terms of spatial-temporal consistency regularization, we force the consistency between the temporally coherent point cloud features and their corresponding image features. We conduct experiments on the nuScenes and SemanticKITTI datasets. For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20.8\% mIoU. When fine-tuned with 1\% or 100\% labelled data, our method significantly outperforms other self-supervised methods, with improvements of 8\% and 1\% mIoU, respectively. Furthermore, we demonstrate its generalization capability for handling cross-domain datasets.
PDF
点此查看论文截图