PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition

• URL: http://arxiv.org/abs/2107.00337v1
• Date: Thu, 1 Jul 2021 10:02:44 GMT
• Title: PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
• Authors: Chiara Plizzari, Mirco Planamente, Emanuele Alberti, Barbara Caputo
• Abstract summary: This report describes our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. We first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA) In a second phase, we extended the approach to work on unlabelled target data, allowing the model to adapt to the target distribution in an unsupervised fashion. Our submission (entry 'plnet') is visible on the leaderboard and it achieved the 1st position for'verb', and the 3rd position for both 'noun' and 'action'
• Score: 15.545769463854915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA). It consists in designing a model able to generalize well to any unseen domain, regardless of the possibility to access target data at training time. Then, in a second phase, we extended the approach to work on unlabelled target data, allowing the model to adapt to the target distribution in an unsupervised fashion. For this purpose, we included in our framework existing UDA algorithms, such as Temporal Attentive Adversarial Adaptation Network (TA3N), jointly with new multi-stream consistency losses, namely Temporal Hard Norm Alignment (T-HNA) and Min-Entropy Consistency (MEC). Our submission (entry 'plnet') is visible on the leaderboard and it achieved the 1st position for 'verb', and the 3rd position for both 'noun' and 'action'.

Related papers

• Progressive Conservative Adaptation for Evolving Target Domains [76.9274842289221]
  Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. Restoring and adapting to such target data results in escalating computational and resource consumption over time. We propose a simple yet effective approach, termed progressive conservative adaptation (PCAda)
  arXiv  Detail & Related papers  (2024-02-07T04:11:25Z)
• SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization [32.35611853688068]
  We introduce, for the first time, an approach for Unsupervised Domain Adaptation in sparse TAL. We pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks. We present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts.
  arXiv  Detail & Related papers  (2023-12-20T19:08:49Z)
• PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition [16.496889090237232]
  This report describes the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation Challenge in Action Recognition. We first exploited a recent Domain Generalization technique, called Relative Norm Alignment (RNA) Secondly, we extended this approach to work on unlabelled target data, enabling a simpler adaptation of the model to the target distribution in an unsupervised fashion.
  arXiv  Detail & Related papers  (2022-09-09T21:03:11Z)
• Prior Knowledge Guided Unsupervised Domain Adaptation [82.9977759320565]
  We propose a Knowledge-guided Unsupervised Domain Adaptation (KUDA) setting where prior knowledge about the target class distribution is available. In particular, we consider two specific types of prior knowledge about the class distribution in the target domain: Unary Bound and Binary Relationship. We propose a rectification module that uses such prior knowledge to refine model generated pseudo labels.
  arXiv  Detail & Related papers  (2022-07-18T18:41:36Z)
• Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021 [6.614021153407064]
  The EPIC-KITCHENS-100 dataset consists of daily kitchen activities focusing on the interaction between human hands and their surrounding objects. It is very challenging to accurately recognize these fine-grained activities, due to the presence of distracting objects and visually similar action classes. We propose to learn hand-centric features by leveraging the hand bounding box information for UDA on fine-grained action recognition. Our submission achieved the 1st place in terms of top-1 action recognition accuracy, using only RGB and optical flow modalities as input.
  arXiv  Detail & Related papers  (2022-06-03T07:37:48Z)
• The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization [10.274423413222763]
  Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. We propose Dynamic Unsupervised Adaptation (DUA) to overcome this problem.
  arXiv  Detail & Related papers  (2021-12-01T12:43:41Z)
• Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework [50.88463458896428]
  Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. Existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. We propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module.
  arXiv  Detail & Related papers  (2021-11-30T02:35:51Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Domain Adaptation with Incomplete Target Domains [61.68950959231601]
  We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains.
  arXiv  Detail & Related papers  (2020-12-03T00:07:40Z)
• Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID [62.90727103061876]
  Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. We propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term. Our proposed framework is shown to achieve state-of-the-art performance on multiple UDA tasks of person re-ID.
  arXiv  Detail & Related papers  (2020-03-14T14:45:18Z)

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.