Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

• URL: http://arxiv.org/abs/2404.07504v1
• Date: Thu, 11 Apr 2024 06:39:53 GMT
• Title: Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
• Authors: Yanhao Wu, Tong Zhang, Wei Ke, Congpei Qiu, Sabine Susstrunk, Mathieu Salzmann,
• Abstract summary: We introduce a novel self-supervised learning (SSL) strategy for point cloud scene understanding. Our approach leverages both object patterns and contextual cues to produce robust features. Our experiments demonstrate the superiority of our method over existing SSL techniques.
• Score: 50.45953583802282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can create a tendency for neural networks to exploit these strong dependencies, bypassing the individual object patterns. To address this challenge, we introduce a novel self-supervised learning (SSL) strategy. Our approach leverages both object patterns and contextual cues to produce robust features. It begins with the formulation of an object-exchanging strategy, where pairs of objects with comparable sizes are exchanged across different scenes, effectively disentangling the strong contextual dependencies. Subsequently, we introduce a context-aware feature learning strategy, which encodes object patterns without relying on their specific context by aggregating object features across various scenes. Our extensive experiments demonstrate the superiority of our method over existing SSL techniques, further showing its better robustness to environmental changes. Moreover, we showcase the applicability of our approach by transferring pre-trained models to diverse point cloud datasets.

Related papers

• Point Cloud Understanding via Attention-Driven Contrastive Learning [64.65145700121442]
  Transformer-based models have advanced point cloud understanding by leveraging self-attention mechanisms. PointACL is an attention-driven contrastive learning framework designed to address these limitations. Our method employs an attention-driven dynamic masking strategy that guides the model to focus on under-attended regions.
  arXiv  Detail & Related papers  (2024-11-22T05:41:00Z)
• Cycle Consistency Driven Object Discovery [75.60399804639403]
  We introduce a method that explicitly optimize the constraint that each object in a scene should be associated with a distinct slot. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
  arXiv  Detail & Related papers  (2023-06-03T21:49:06Z)
• De-coupling and De-positioning Dense Self-supervised Learning [65.56679416475943]
  Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. We show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection.
  arXiv  Detail & Related papers  (2023-03-29T18:07:25Z)
• Spatiotemporal Self-supervised Learning for Point Clouds in the Wild [65.56679416475943]
  We introduce an SSL strategy that leverages positive pairs in both the spatial and temporal domain. We demonstrate the benefits of our approach via extensive experiments performed by self-supervised training on two large-scale LiDAR datasets.
  arXiv  Detail & Related papers  (2023-03-28T18:06:22Z)
• Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection [37.06088084592779]
  We conduct an empirical study of contrastive learning and out-of-domain object detection. We propose strategies to augment views and enhance robustness in appearance-shifted and context-shifted scenarios. Our results and insights show how to ensure robustness through the choice of views in contrastive learning.
  arXiv  Detail & Related papers  (2022-12-09T00:34:50Z)
• Reason from Context with Self-supervised Learning [15.16197896174348]
  We propose a new Self-supervised method with external memories for Context Reasoning (SeCo) In both tasks, SeCo outperformed all state-of-the-art (SOTA) SSL methods by a significant margin. Our results demonstrate that SeCo exhibits human-like behaviors.
  arXiv  Detail & Related papers  (2022-11-23T10:02:05Z)
• SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition [35.4163266882568]
  We introduce Self-Supervised Learning Over Sets (SOS) to pre-train a generic Objects In Contact (OIC) representation model. Our OIC significantly boosts the performance of multiple state-of-the-art video classification models.
  arXiv  Detail & Related papers  (2022-04-10T23:27:19Z)
• Object Pursuit: Building a Space of Objects via Discriminative Weight Generation [23.85039747700698]
  We propose a framework to continuously learn object-centric representations for visual learning and understanding. We leverage interactions to sample diverse variations of an object and the corresponding training signals while learning the object-centric representations. We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
  arXiv  Detail & Related papers  (2021-12-15T08:25:30Z)
• Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation [53.49821324597837]
  Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-03-02T15:05:09Z)
• Structure Mapping for Transferability of Causal Models [10.697752818461893]
  We design a transfer-learning framework using object-oriented representations to learn causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects.
  arXiv  Detail & Related papers  (2020-07-18T14:59:54Z)

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.