Matching Multiple Perspectives for Efficient Representation Learning

• URL: http://arxiv.org/abs/2208.07654v1
• Date: Tue, 16 Aug 2022 10:33:13 GMT
• Title: Matching Multiple Perspectives for Efficient Representation Learning
• Authors: Omiros Pantazis, Mathew Salvaris
• Abstract summary: We present an approach that combines self-supervised learning with a multi-perspective matching technique. We show that the availability of multiple views of the same object combined with a variety of self-supervised pretraining algorithms can lead to improved object classification performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Representation learning approaches typically rely on images of objects captured from a single perspective that are transformed using affine transformations. Additionally, self-supervised learning, a successful paradigm of representation learning, relies on instance discrimination and self-augmentations which cannot always bridge the gap between observations of the same object viewed from a different perspective. Viewing an object from multiple perspectives aids holistic understanding of an object which is particularly important in situations where data annotations are limited. In this paper, we present an approach that combines self-supervised learning with a multi-perspective matching technique and demonstrate its effectiveness on learning higher quality representations on data captured by a robotic vacuum with an embedded camera. We show that the availability of multiple views of the same object combined with a variety of self-supervised pretraining algorithms can lead to improved object classification performance without extra labels.

Related papers

• Understanding Self-Supervised Pretraining with Part-Aware Representation Learning [88.45460880824376]
  We study the capability that self-supervised representation pretraining methods learn part-aware representations. Results show that the fully-supervised model outperforms self-supervised models for object-level recognition.
  arXiv  Detail & Related papers  (2023-01-27T18:58:42Z)
• On the robustness of self-supervised representations for multi-view object classification [0.0]
  We show that self-supervised representations based on the instance discrimination objective lead to better representations of objects that are more robust to changes in the viewpoint and perspective of the object. We find that self-supervised representations are more robust to object viewpoint and appear to encode more pertinent information about objects that facilitate the recognition of objects from novel views.
  arXiv  Detail & Related papers  (2022-07-27T17:24:55Z)
• Self-Supervised Visual Representation Learning with Semantic Grouping [50.14703605659837]
  We tackle the problem of learning visual representations from unlabeled scene-centric data. We propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning.
  arXiv  Detail & Related papers  (2022-05-30T17:50:59Z)
• Towards Self-Supervised Learning of Global and Object-Centric Representations [4.36572039512405]
  We discuss key aspects of learning structured object-centric representations with self-supervision. We validate our insights through several experiments on the CLEVR dataset.
  arXiv  Detail & Related papers  (2022-03-11T15:18:47Z)
• 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)
• Object-aware Contrastive Learning for Debiased Scene Representation [74.30741492814327]
  We develop a novel object-aware contrastive learning framework that localizes objects in a self-supervised manner. We also introduce two data augmentations based on ContraCAM, object-aware random crop and background mixup, which reduce contextual and background biases during contrastive self-supervised learning.
  arXiv  Detail & Related papers  (2021-07-30T19:24:07Z)
• Self-Supervised Representation Learning from Flow Equivariance [97.13056332559526]
  We present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes. Our representations, learned from high-resolution raw video, can be readily used for downstream tasks on static images.
  arXiv  Detail & Related papers  (2021-01-16T23:44:09Z)
• Self-supervised Learning from a Multi-view Perspective [121.63655399591681]
  We show that self-supervised representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design.
  arXiv  Detail & Related papers  (2020-06-10T00:21:35Z)
• Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition [66.87417785210772]
  This work investigates the problem of multiview self-supervised learning (MV-SSL) A novel surrogate task for self-supervised learning is proposed by pursuing "object invariant" representation. Experiments shows that the recognition and retrieval results using view invariant prototype embedding (VISPE) outperform other self-supervised learning methods.
  arXiv  Detail & Related papers  (2020-03-28T07:06:06Z)

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.