EgoPrompt: Prompt Learning for Egocentric Action Recognition

• URL: http://arxiv.org/abs/2508.03266v2
• Date: Thu, 07 Aug 2025 07:36:17 GMT
• Title: EgoPrompt: Prompt Learning for Egocentric Action Recognition
• Authors: Huaihai Lyu, Chaofan Chen, Yuheng Ji, Changsheng Xu,
• Abstract summary: EgoPrompt is a prompt learning-based framework to conduct egocentric action recognition task.<n>EgoPrompt achieves state-of-the-art performance across within-dataset, cross-dataset, and base-to-novel generalization benchmarks.
• Score: 49.12318087940015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the increasing demand for applications in augmented and virtual reality, egocentric action recognition has emerged as a prominent research area. It is typically divided into two subtasks: recognizing the performed behavior (i.e., verb component) and identifying the objects being acted upon (i.e., noun component) from the first-person perspective. However, most existing approaches treat these two components as independent classification tasks, focusing on extracting component-specific knowledge while overlooking their inherent semantic and contextual relationships, leading to fragmented representations and sub-optimal generalization capability. To address these challenges, we propose a prompt learning-based framework, EgoPrompt, to conduct the egocentric action recognition task. Building on the existing prompting strategy to capture the component-specific knowledge, we construct a Unified Prompt Pool space to establish interaction between the two types of component representations. Specifically, the component representations (from verbs and nouns) are first decomposed into fine-grained patterns with the prompt pair form. Then, these pattern-level representations are fused through an attention-based mechanism to facilitate cross-component interaction. To ensure the prompt pool is informative, we further introduce a novel training objective, Diverse Pool Criteria. This objective realizes our goals from two perspectives: Prompt Selection Frequency Regularization and Prompt Knowledge Orthogonalization. Extensive experiments are conducted on the Ego4D, EPIC-Kitchens, and EGTEA datasets. The results consistently show that EgoPrompt achieves state-of-the-art performance across within-dataset, cross-dataset, and base-to-novel generalization benchmarks.

Related papers

• Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation [52.6091162517921]
  INSIGHT is a two-stage framework for egocentric action anticipation.<n>In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions.<n>In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning.
  arXiv  Detail & Related papers  (2025-08-03T12:52:27Z)
• Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning [27.511627003202538]
  Traditional scene graphs primarily focus on spatial relationships, limiting vision-language models' (VLMs) ability to reason about complex interactions in visual scenes.<n>This paper addresses two key challenges: (1) conventional detection-to-construction methods produce unfocused, contextually irrelevant relationship sets, and (2) existing approaches fail to form persistent memories for generalizing interaction reasoning to new scenes.<n>We propose Interaction-augmented Scene Graph Reasoning (ISGR), a framework that enhances VLMs' interactional reasoning through three complementary components.
  arXiv  Detail & Related papers  (2025-05-14T04:04:23Z)
• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction.<n>To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism.<n>Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition [21.655278000690686]
  We propose an end-to-end object-centric action recognition framework. It simultaneously performs Detection And Interaction Reasoning in one stage. We conduct experiments on two datasets, Something-Else and Ikea-Assembly.
  arXiv  Detail & Related papers  (2024-04-18T05:06:12Z)
• Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
  We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models. Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
  arXiv  Detail & Related papers  (2023-08-22T04:24:45Z)
• Compositional Learning in Transformer-Based Human-Object Interaction Detection [6.630793383852106]
  Long-tailed distribution of labeled instances is a primary challenge in HOI detection. Inspired by the nature of HOI triplets, some existing approaches adopt the idea of compositional learning. We creatively propose a transformer-based framework for compositional HOI learning.
  arXiv  Detail & Related papers  (2023-08-11T06:41:20Z)
• Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition [57.86960990337986]
  One-shot skeleton-based action recognition poses unique challenges in learning transferable representation from base classes to novel classes. We propose a part-aware prototypical representation for one-shot skeleton-based action recognition. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets.
  arXiv  Detail & Related papers  (2022-08-19T04:54:56Z)
• 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)
• Phrase-Based Affordance Detection via Cyclic Bilateral Interaction [17.022853987801877]
  We explore to perceive affordance from a vision-language perspective and consider the challenging phrase-based affordance detection problem. We propose a cyclic bilateral consistency enhancement network (CBCE-Net) to align language and vision features progressively. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a cyclic interaction module (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner.
  arXiv  Detail & Related papers  (2022-02-24T13:02:27Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.