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Related Concept Videos

Vision01:24

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.
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language and Cognition01:27

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.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Related Experiment Video

Updated: Jun 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Revisiting InternVL: A Systematic Technical Framework for Building Powerful Open-Source Vision-Language Models.

Zhe Chen, Weiyun Wang, Jinguo Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study details the evolution of the InternVL vision-language model (VLM) series, presenting a framework for building high-performance VLMs through perceptual scaling, multimodal alignment scaling, and native multimodal pre-training. The framework achieves state-of-the-art results, rivaling proprietary systems.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Developing powerful vision-language models (VLMs) requires a comprehensive system design.
    • The InternVL series (v1.0-v3.0) represents a significant advancement in VLM research.
    • Existing approaches often lack a systematic framework for scaling and performance optimization.

    Purpose of the Study:

    • To present a systematic framework for constructing large-scale, high-performance VLMs based on the InternVL series evolution.
    • To detail three pivotal technical shifts: Perceptual Scaling, Multimodal Alignment Scaling, and Native Multimodal Pre-training.
    • To offer a reproducible roadmap for future multimodal research.

    Main Methods:

    • Developed a 6-billion parameter vision encoder (InternViT-6B) and a VLM-oriented alignment strategy for fine-grained perception.
    • Implemented a multimodal dynamic high-resolution (mDHR) mechanism for unified input handling (single-image, multi-image, video).
    • Transitioned to a native multimodal continual pre-training paradigm, jointly optimizing interleaved multimodal and text-only data.

    Main Results:

    • Models built on the framework achieve state-of-the-art performance among open-source VLMs.
    • Performance rivals leading proprietary vision-language systems across various benchmarks.
    • Demonstrated deep synergy between visual-world knowledge internalization and preserved linguistic proficiency.

    Conclusions:

    • The presented framework provides a systematic and reproducible approach to building high-performance VLMs.
    • The technical shifts in perceptual and multimodal alignment scaling are crucial for advancing VLM capabilities.
    • Native multimodal pre-training enhances model synergy and performance, offering a path for future research.