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Related Concept Videos

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Deductive Reasoning

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Reasoning

Reasoning

Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

Vision

Vision

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.

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Related Experiment Video

Updated: Nov 7, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning.

JaeYun Lee¹, Incheol Kim¹

¹Department of Computer Science, Kyonggi University, Suwon-si 16227, Korea.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

This study introduces a new Vision-Language-Knowledge Co-embedding (ViLaKC) model for visual commonsense reasoning. The ViLaKC model effectively integrates external knowledge graphs to improve image and text understanding for answering questions.

Keywords:

graph convolutional network knowledge graph multimodal co-embedding pretrained multi-head self-attention network visual commonsense reasoning

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Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Visual commonsense reasoning requires solving knowledge acquisition and multimodal alignment.
Existing models face challenges in integrating external knowledge effectively.

Purpose of the Study:

To propose a novel Vision-Language-Knowledge Co-embedding (ViLaKC) model.
To enhance visual commonsense reasoning by incorporating external knowledge graphs.

Main Methods:

The ViLaKC model extracts relevant knowledge graphs from ConceptNet.
It employs a pretrained vision-language-knowledge embedding module.
Graph convolutional neural networks and multi-head self-attention layers are used for co-embedding multimodal data.

Main Results:

The ViLaKC model demonstrates effectiveness and strong performance.
Experimental validation was conducted using the VCR v1.0 benchmark dataset.

Conclusions:

The proposed ViLaKC model offers a robust approach to visual commonsense reasoning.
Integrating external knowledge graphs significantly improves reasoning capabilities.