Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Development and characterization of monoclonal antibodies to spring viraemia of carp virus.

Veterinary immunology and immunopathology·2008

Same author

Electrophysiological characterization of a novel Kv channel blocker N,N'-[oxybis(2,1-ethanediyloxy-2,1-ethanediyl) ]bis(4-methyl)-benzenesulfonamide found in virtual screening.

Acta pharmacologica Sinica·2008

Same author

Cloning and characterization of an rRNA methyltransferase from Sorangium cellulosum.

Biochemical and biophysical research communications·2008

Same author

Reconstruction of anophthalmic orbits and contracted eye sockets with microvascular radial forearm free flaps.

Ophthalmic plastic and reconstructive surgery·2008

Same author

Using low intensity ultrasound to improve the efficiency of biological phosphorus removal.

Ultrasonics sonochemistry·2008

Same author

[Study on the spectrum of the flocculent conformation of polymer ferric sulfate flocculants].

Guang pu xue yu guang pu fen xi = Guang pu·2008

Same journal

Confidence-driven adaptive time window for real-time driver fatigue detection in Level 2-3 autonomous vehicles: a multi-dataset validation study.

Frontiers in neurorobotics·2026

Same journal

DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

Frontiers in neurorobotics·2026

Same journal

ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

Frontiers in neurorobotics·2026

Same journal

ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

Frontiers in neurorobotics·2026

Same journal

EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

Frontiers in neurorobotics·2026

Same journal

Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

Frontiers in neurorobotics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Dense captioning and multidimensional evaluations for indoor robotic scenes.

Hua Wang^1,2, Wenshuai Wang¹, Wenhao Li¹

¹Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Shenzhen, China.

Frontiers in Neurorobotics

|November 30, 2023

Summary

This summary is machine-generated.

This study presents RGBD2Cap, a new method for generating scene descriptions using RGBD images. It improves upon existing methods by integrating depth and color data for richer scene understanding in human-computer interaction.

Keywords:

RGBD fusion dense captioning indoor robotic scene multidimensional evaluation top-down attention

More Related Videos

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

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

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

Related Experiment Videos

Last Updated: Jul 9, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

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

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

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

Area of Science:

Computer Vision
Human-Computer Interaction
Artificial Intelligence

Background:

Scene understanding is vital for intelligent human-computer interaction.
Generating semantic descriptions from visual data remains a significant challenge.
Existing methods often struggle with integrating multi-modal scene information.

Purpose of the Study:

To introduce RGBD2Cap, a novel method for scene semantic description using RGBD images.
To enhance scene understanding by effectively fusing RGB and Depth information.
To improve the accuracy and richness of generated scene descriptions.

Main Methods:

Utilized a multimodal fusion module to integrate RGB and Depth data for multi-level feature extraction.
Incorporated target detection, region proposal network, and an attention LSTM network for description generation.
Trained and evaluated the model on the ScanRefer dataset using rendered ScanNet 3D scenes.

Main Results:

RGBD2Cap outperformed the DenseCap network in BLEU, CIDEr, and METEOR metrics.
Ablation studies confirmed the critical contribution of the RGBD fusion module.
The method demonstrated practical applicability and reliability in the AI2-THOR environment.

Conclusions:

RGBD2Cap offers a robust approach to scene semantic description using RGBD data.
Multimodal fusion of RGB and Depth information significantly enhances scene understanding.
The method shows promise for advancing embodied intelligence and human-computer interaction systems.