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Updated: Jun 18, 2026
08:25
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition
Summary
State-of-the-art open-vocabulary action recognition models fail to generalize across video domains. Our novel XOV-Action model enhances generalization to new action categories and unseen video domains.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Image-text foundation models show success, leading to video adaptation for open-vocabulary action recognition.
- Current models struggle with generalization to diverse, unseen video domains and novel action categories.
- Generalizable open-vocabulary action recognition aims to improve model robustness across domains and action sets.
Purpose of the Study:
- To develop a novel model, XOV-Action, for generalizable open-vocabulary action recognition.
- To address challenges in understanding novel action concepts and mitigating domain discrepancies.
- To introduce XOVABench, a new benchmark for evaluating cross-domain action recognition.
Main Methods:
- XOV-Action learns diversified elaboration representations for better open-set action generalization.
- Scene-agnostic video representations are learned to overcome scene bias and improve domain generalization.
- A new cross-domain action benchmark, XOVABench, is introduced for comprehensive evaluation.
Main Results:
- XOV-Action demonstrates improved performance on both closed-set and open-set action categories.
- The model shows enhanced generalization capabilities across various video domains.
- Experiments validate the effectiveness of XOV-Action in addressing domain shift and novel categories.
Conclusions:
- XOV-Action effectively improves generalizable open-vocabulary action recognition.
- The proposed methods enhance model robustness to unseen domains and action categories.
- XOVABench provides a valuable resource for future research in cross-domain action recognition.
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