DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification

• URL: http://arxiv.org/abs/2305.15957v3
• Date: Mon, 6 May 2024 16:15:50 GMT
• Title: DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
• Authors: Sitian Shen, Zilin Zhu, Linqian Fan, Harry Zhang, Xinxiao Wu,
• Abstract summary: This paper proposes DiffCLIP, a new pre-training framework that incorporates stable diffusion with ControlNet to minimize the domain gap in the visual branch. Experiments on the ModelNet10, ModelNet40, and ScanObjectNN datasets show that DiffCLIP has strong abilities for 3D understanding.
• Score: 19.40810553327253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation. However, the model's performance on 3D point cloud processing tasks is limited due to the domain gap between depth maps from 3D projection and training images of CLIP. This paper proposes DiffCLIP, a new pre-training framework that incorporates stable diffusion with ControlNet to minimize the domain gap in the visual branch. Additionally, a style-prompt generation module is introduced for few-shot tasks in the textual branch. Extensive experiments on the ModelNet10, ModelNet40, and ScanObjectNN datasets show that DiffCLIP has strong abilities for 3D understanding. By using stable diffusion and style-prompt generation, DiffCLIP achieves an accuracy of 43.2\% for zero-shot classification on OBJ\_BG of ScanObjectNN, which is state-of-the-art performance, and an accuracy of 80.6\% for zero-shot classification on ModelNet10, which is comparable to state-of-the-art performance.

Related papers

• CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge [18.57081150228812]
  We propose a novel 6D pose framework that employs the pre-trained vision-language model to develop better learning of object category information. CLIPose achieves state-of-the-art performance on two mainstream benchmark datasets, REAL275 and CAMERA25, and runs in real-time during inference (40FPS)
  arXiv  Detail & Related papers  (2024-02-24T05:31:53Z)
• MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition [49.52436478739151]
  Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Recent methods utilize language-image pre-training to realize zero-shot 3D shape recognition. This paper aims to improve the confidence with view selection and hierarchical prompts.
  arXiv  Detail & Related papers  (2023-11-30T09:51:53Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding [110.07170245531464]
  Current 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. Recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. We learn a unified representation of images, texts, and 3D point clouds by pre-training with object triplets from the three modalities.
  arXiv  Detail & Related papers  (2022-12-10T01:34:47Z)
• CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training [121.46758260964114]
  Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. We propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain.
  arXiv  Detail & Related papers  (2022-10-03T16:13:14Z)
• Masked Unsupervised Self-training for Zero-shot Image Classification [98.23094305347709]
  Masked Unsupervised Self-Training (MUST) is a new approach which leverages two different and complimentary sources of supervision: pseudo-labels and raw images. MUST improves upon CLIP by a large margin and narrows the performance gap between unsupervised and supervised classification.
  arXiv  Detail & Related papers  (2022-06-07T02:03:06Z)
• PointCLIP: Point Cloud Understanding by CLIP [77.02399444893963]
  We propose PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D category texts. PointCLIP is a promising alternative for effective 3D point cloud understanding via CLIP under low resource cost and data regime.
  arXiv  Detail & Related papers  (2021-12-04T19:42:40Z)
• Learning Compositional Shape Priors for Few-Shot 3D Reconstruction [36.40776735291117]
  We show that complex encoder-decoder architectures exploit large amounts of per-category data. We propose three ways to learn a class-specific global shape prior, directly from data. Experiments on the popular ShapeNet dataset show that our method outperforms a zero-shot baseline by over 40%.
  arXiv  Detail & Related papers  (2021-06-11T14:55:49Z)
• Point Transformer for Shape Classification and Retrieval of 3D and ALS Roof PointClouds [3.3744638598036123]
  This paper proposes a fully attentional model - em Point Transformer, for deriving a rich point cloud representation. The model's shape classification and retrieval performance are evaluated on a large-scale urban dataset - RoofN3D and a standard benchmark dataset ModelNet40. The proposed method outperforms other state-of-the-art models in the RoofN3D dataset, gives competitive results in the ModelNet40 benchmark, and showcases high robustness to various unseen point corruptions.
  arXiv  Detail & Related papers  (2020-11-08T08:11:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.