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相关概念视频

Vision01:24

Vision

52.8K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.8K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

501
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

Updated: May 22, 2025

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

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通过视觉语言集成实现零射击的人物对象交互检测.

Weiying Xue1, Qi Liu1, Yuxiao Wang1

  • 1School of Future Technology, South China University of Technology, Guangdong Guangzhou, 511400, PR China.

Neural networks : the official journal of the International Neural Network Society
|March 14, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了人类与物体交互 (HOI) 检测的新框架,通过整合视觉语言模型 (VLMs) 来提高零射击能力. KI2HOI模型提高了HOI检测的准确性,特别是在看不见的物体类别.

关键词:
人类与物体的互动.多式联运集成多式联运集成软弱的监督 监督的软弱一个零射击.

更多相关视频

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

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Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
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Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

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相关实验视频

Last Updated: May 22, 2025

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

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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

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Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人物对象交互 (HOI) 检测对于理解图像中的复杂场景至关重要.
  • 目前的监督方法由于依赖广泛的注释,难以将其推广到看不见的对象类别.
  • 视觉语言模型 (VLM) 显示了零射击学习的前景,为HOI检测局限性提供了潜在的解决方案.

研究的目的:

  • 开发一种新的框架,即知识集成到HOI (KI2HOI),用于改进零射击HOI检测.
  • 利用VLM的零射击能力来提高HOI检测性能.
  • 为了解决当前HOI检测方法的概括限制.

主要方法:

  • 提出了一个新的框架,KI2HOI,整合了VLM知识,用于零射击HOI检测.
  • 引入了一个ho-pair编码器,以提供上下文和交互特定的语义表示.
  • 实施了两种融合策略:用于全球背景的视觉级融合和语言级融合,以增强VLM能力.

主要成果:

  • 与HICO-DET和V-COCO数据集上的现有方法相比,KI2HOI表现出更高的性能.
  • 该模型在零射击和完全监督的HOI检测设置中取得了显著的改进.
  • 拟议的融合战略有效地促进了VLMs的先前知识转移.

结论:

  • KI2HOI框架有效地整合了VLM知识,以推进零射击HOI检测.
  • 该方法表现出强大的概括能力,性能优于以前的方法.
  • 这项工作为更强大,更适应性强的HOI检测系统提供了有希望的方向.