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Related Experiment Video

Updated: Jun 26, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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

Language-Driven Visual Data Generation for Zero-Shot HOI Detection.

Pei Geng, Shanshan Zhang, Jian Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Language-Driven Visual Data Generation (LD-VDG) method to improve zero-shot human-object interaction (HOI) detection. LD-VDG generates visual features from text, enabling better recognition of unseen HOI categories.

    Related Experiment Videos

    Last Updated: Jun 26, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    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

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Zero-shot human-object interaction (HOI) detection aims to identify interactions between humans and objects, including categories not seen during training.
    • Existing methods struggle with unseen HOI categories due to a lack of training data, leading to overfitting on seen interactions and poor generalization.
    • This limitation hinders the practical application of HOI detection in diverse, real-world scenarios.

    Purpose of the Study:

    • To develop a novel approach for zero-shot HOI detection that effectively generalizes to unseen interaction categories.
    • To overcome the data scarcity problem for unseen HOIs by leveraging textual information.
    • To enhance the performance of HOI detection systems on both seen and unseen interaction types.

    Main Methods:

    • Introduced a Language-Driven Visual Data Generation (LD-VDG) approach to create pseudo visual features from textual semantics of unseen HOIs.
    • Designed a text-to-vision (T-V) adapter, trained on seen HOIs, to align text and visual features.
    • Utilized a large language model to generate fine-grained textual descriptions for unseen HOIs, which were then transformed into pseudo visual features via the T-V adapter.

    Main Results:

    • The generated pseudo visual features, combined with real features from seen HOIs, were used to train a transformer-based HOI detector.
    • Experimental results on standard datasets demonstrated that LD-VDG significantly outperforms previous methods in zero-shot HOI detection.
    • The method achieved superior performance specifically on unseen HOI categories across various zero-shot settings.

    Conclusions:

    • The proposed LD-VDG approach offers an innovative solution for generalizing to unseen HOIs by generating language-driven visual representations.
    • This method effectively addresses the challenge of data scarcity for unseen categories in HOI detection.
    • LD-VDG demonstrates the potential of leveraging textual semantics to enhance visual recognition tasks, particularly in zero-shot learning scenarios.