Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification

• URL: http://arxiv.org/abs/2404.00857v1
• Date: Mon, 1 Apr 2024 01:56:27 GMT
• Title: Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification
• Authors: Shuvozit Ghose, Yang Wang,
• Abstract summary: We propose a novel meta-episodic learning framework for CLIP-based point cloud classification. We introduce dynamic task sampling within the episode based on performance memory. Experiments show an average performance gain of 3-6% on ModelNet40 and ScanobjectNN datasets.
• Score: 6.195058023850564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud classification refers to the process of assigning semantic labels or categories to individual points within a point cloud data structure. Recent works have explored the extension of pre-trained CLIP to 3D recognition. In this direction, CLIP-based point cloud models like PointCLIP, CLIP2Point have become state-of-the-art methods in the few-shot setup. Although these methods show promising performance for some classes like airplanes, desks, guitars, etc, the performance for some classes like the cup, flower pot, sink, nightstand, etc is still far from satisfactory. This is due to the fact that the adapter of CLIP-based models is trained using randomly sampled N-way K-shot data in the standard supervised learning setup. In this paper, we propose a novel meta-episodic learning framework for CLIP-based point cloud classification, addressing the challenges of limited training examples and sampling unknown classes. Additionally, we introduce dynamic task sampling within the episode based on performance memory. This sampling strategy effectively addresses the challenge of sampling unknown classes, ensuring that the model learns from a diverse range of classes and promotes the exploration of underrepresented categories. By dynamically updating the performance memory, we adaptively prioritize the sampling of classes based on their performance, enhancing the model's ability to handle challenging and real-world scenarios. Experiments show an average performance gain of 3-6\% on ModelNet40 and ScanobjectNN datasets in a few-shot setup.

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