Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions

• URL: http://arxiv.org/abs/2508.05529v1
• Date: Thu, 07 Aug 2025 16:00:00 GMT
• Title: Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions
• Authors: Federico Spurio, Emad Bahrami, Olga Zatsarynna, Yazan Abu Farha, Gianpiero Francesca, Juergen Gall,
• Abstract summary: We introduce Action Discovery, a novel setup that addresses the challenge of defining and annotating ambiguous actions in datasets.<n>In this setup, only a subset of actions - referred to as known actions - is annotated in the training data, while other unknown actions remain unlabeled.<n>This scenario is particularly relevant in domains like neuroscience, where well-defined behaviors coexist with subtle or infrequent actions that are often overlooked.
• Score: 16.42937388046283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Action Discovery, a novel setup within Temporal Action Segmentation that addresses the challenge of defining and annotating ambiguous actions and incomplete annotations in partially labeled datasets. In this setup, only a subset of actions - referred to as known actions - is annotated in the training data, while other unknown actions remain unlabeled. This scenario is particularly relevant in domains like neuroscience, where well-defined behaviors (e.g., walking, eating) coexist with subtle or infrequent actions that are often overlooked, as well as in applications where datasets are inherently partially annotated due to ambiguous or missing labels. To address this problem, we propose a two-step approach that leverages the known annotations to guide both the temporal and semantic granularity of unknown action segments. First, we introduce the Granularity-Guided Segmentation Module (GGSM), which identifies temporal intervals for both known and unknown actions by mimicking the granularity of annotated actions. Second, we propose the Unknown Action Segment Assignment (UASA), which identifies semantically meaningful classes within the unknown actions, based on learned embedding similarities. We systematically explore the proposed setting of Action Discovery on three challenging datasets - Breakfast, 50Salads, and Desktop Assembly - demonstrating that our method considerably improves upon existing baselines.

Related papers

• Proposal-based Temporal Action Localization with Point-level Supervision [29.98225940694062]
  Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos. We propose a novel method that localizes actions by generating and evaluating action proposals of flexible duration. Experiments show that our proposed method achieves competitive or superior performance to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-09T08:27:05Z)
• Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature [26.7937345622207]
  Weakly-supervised temporal action localization aims to locate action regions and identify action categories in unsupervised videos simultaneously. Pseudo label generation is a promising strategy to solve the challenging problem, but the current methods ignore the natural temporal structure of the video. We propose a novel weakly-supervised temporal action localization method by inferring salient snippet-feature.
  arXiv  Detail & Related papers  (2023-03-22T06:08:34Z)
• Segmenting Known Objects and Unseen Unknowns without Prior Knowledge [86.46204148650328]
  holistic segmentation aims to identify and separate objects of unseen, unknown categories into instances without any prior knowledge about them. We tackle this new problem with U3HS, which finds unknowns as highly uncertain regions and clusters their corresponding instance-aware embeddings into individual objects. Experiments on public data from MS, Cityscapes, and Lost&Found demonstrate the effectiveness of U3HS.
  arXiv  Detail & Related papers  (2022-09-12T16:59:36Z)
• SegTAD: Precise Temporal Action Detection via Semantic Segmentation [65.01826091117746]
  We formulate the task of temporal action detection in a novel perspective of semantic segmentation. Owing to the 1-dimensional property of TAD, we are able to convert the coarse-grained detection annotations to fine-grained semantic segmentation annotations for free. We propose an end-to-end framework SegTAD composed of a 1D semantic segmentation network (1D-SSN) and a proposal detection network (PDN)
  arXiv  Detail & Related papers  (2022-03-03T06:52:13Z)
• Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning [51.03781020616402]
  Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications. We propose a few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions.
  arXiv  Detail & Related papers  (2021-08-15T02:21:01Z)
• Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context [151.23835595907596]
  Methods learn to localize temporal starts and ends of action instances in a video under only video-level supervision. We introduce a framework that learns two feature subspaces respectively for actions and their context. The proposed approach outperforms state-of-the-art WS-TAL methods on three benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T08:26:53Z)
• Sparse Semi-Supervised Action Recognition with Active Learning [10.558951653323286]
  Current state-of-the-art methods for skeleton-based action recognition are supervised and rely on labels. We propose a novel approach for skeleton-based action recognition, called SESAR, that connects these approaches. Our results outperform standalone skeleton-based supervised, unsupervised with cluster identification, and active-learning methods for action recognition when applied to sparse labeled samples.
  arXiv  Detail & Related papers  (2020-12-03T07:48:31Z)
• Intra- and Inter-Action Understanding via Temporal Action Parsing [118.32912239230272]
  We construct a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. Our study shows that a sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition. We also investigate a number of temporal parsing methods, and thereon devise an improved method that is capable of mining sub-actions from training data without knowing the labels of them.
  arXiv  Detail & Related papers  (2020-05-20T17:45:18Z)
• FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding [118.32912239230272]
  FineGym is a new action recognition dataset built on top of gymnastic videos. It provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. This new level of granularity presents significant challenges for action recognition.
  arXiv  Detail & Related papers  (2020-04-14T17:55:21Z)

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.