DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning

• URL: http://arxiv.org/abs/2004.13458v4
• Date: Thu, 10 Sep 2020 16:19:05 GMT
• Title: DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
• Authors: Timo Milbich, Karsten Roth, Homanga Bharadhwaj, Samarth Sinha, Yoshua Bengio, Bj\"orn Ommer, and Joseph Paul Cohen
• Abstract summary: Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities. We propose and study multiple complementary learning tasks, targeting conceptually different data relationships. We learn a single model to aggregate their training signals, resulting in strong generalization and state-of-the-art performance.
• Score: 83.48587570246231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only generalize from training data to identically distributed test distributions, but in particular also translate to unknown test classes. However, its prevailing learning paradigm is class-discriminative supervised training, which typically results in representations specialized in separating training classes. For effective generalization, however, such an image representation needs to capture a diverse range of data characteristics. To this end, we propose and study multiple complementary learning tasks, targeting conceptually different data relationships by only resorting to the available training samples and labels of a standard DML setting. Through simultaneous optimization of our tasks we learn a single model to aggregate their training signals, resulting in strong generalization and state-of-the-art performance on multiple established DML benchmark datasets.

Related papers

• Self-Supervised Representation Learning with Meta Comprehensive Regularization [11.387994024747842]
  We introduce a module called CompMod with Meta Comprehensive Regularization (MCR), embedded into existing self-supervised frameworks. We update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features. We provide theoretical support for our proposed method from information theory and causal counterfactual perspective.
  arXiv  Detail & Related papers  (2024-03-03T15:53:48Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Unified Demonstration Retriever for In-Context Learning [56.06473069923567]
  Unified Demonstration Retriever (textbfUDR) is a single model to retrieve demonstrations for a wide range of tasks. We propose a multi-task list-wise ranking training framework, with an iterative mining strategy to find high-quality candidates. Experiments on 30+ tasks across 13 task families and multiple data domains show that UDR significantly outperforms baselines.
  arXiv  Detail & Related papers  (2023-05-07T16:07:11Z)
• The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning [32.15608637930748]
  We show that there exists a trade-off between the two desiderata so that one may not be able to achieve both simultaneously. We provide analysis using a theoretical data model and show that, while more diverse pre-training data result in more diverse features for different tasks, it puts less emphasis on task-specific features.
  arXiv  Detail & Related papers  (2023-02-28T22:14:33Z)
• The Effect of Diversity in Meta-Learning [79.56118674435844]
  Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task distribution plays a vital role in the model's performance. We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
  arXiv  Detail & Related papers  (2022-01-27T19:39:07Z)
• Self-Supervised Visual Representation Learning Using Lightweight Architectures [0.0]
  In self-supervised learning, a model is trained to solve a pretext task, using a data set whose annotations are created by a machine. We critically examine the most notable pretext tasks to extract features from image data. We study the performance of various self-supervised techniques keeping all other parameters uniform.
  arXiv  Detail & Related papers  (2021-10-21T14:13:10Z)
• Multimodal Contrastive Training for Visual Representation Learning [45.94662252627284]
  We develop an approach to learning visual representations that embraces multimodal data. Our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously. By including multimodal training in a unified framework, our method can learn more powerful and generic visual features.
  arXiv  Detail & Related papers  (2021-04-26T19:23:36Z)
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos [69.61522804742427]
  This paper proposes a self-supervised training framework that learns a common multimodal embedding space. We extend the concept of instance-level contrastive learning with a multimodal clustering step to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains.
  arXiv  Detail & Related papers  (2021-04-26T15:55:01Z)
• Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification [17.237331828747006]
  This work focuses on few-shot relation classification (FSRC) We propose an adaptive mixture mechanism to add label words to the representation of the class prototype. Experiments have been conducted on FewRel under different few-shot (FS) settings.
  arXiv  Detail & Related papers  (2021-01-10T11:25:42Z)
• Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
  Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method. PCL implicitly encodes semantic structures of the data into the learned embedding space. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
  arXiv  Detail & Related papers  (2020-05-11T09:53:36Z)

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.