计算机视觉

Search documents
4万多名作者挤破头,CVPR 2025官方揭秘三大爆款主题, 你卷对方向了吗?
机器之心· 2025-05-28 03:02
Core Insights - The article discusses the latest trends in the field of computer vision, highlighting three major research directions that are gaining traction as of 2025 [3][4]. Group 1: Major Research Directions - The three prominent areas identified are: 1. Multi-view and sensor 3D technology, which has evolved from 2D rendering to more complex 3D evaluations, significantly influenced by the introduction of NeRF in 2020 [5]. 2. Image and video synthesis, which has become a focal point for presenting environmental information more accurately, reflecting advancements in the ability to analyze and generate multimedia content [6]. 3. Multimodal learning, which integrates visual, linguistic, and reasoning capabilities, indicating a trend towards more interactive and comprehensive AI systems [7][8]. Group 2: Conference Insights - The CVPR 2025 conference has seen a 13% increase in paper submissions, with a total of 13,008 submissions and an acceptance rate of 22.1%, indicating a highly competitive environment [3]. - The conference emphasizes the importance of diverse voices in the research community, ensuring that every paper, regardless of the author's affiliation, is given equal consideration [8].
长春光机所光电突触器件研究取得新进展
Huan Qiu Wang Zi Xun· 2025-05-10 09:18
Core Insights - The research team at Changchun Institute of Optics, Fine Mechanics and Physics has developed a novel ultraviolet optoelectronic synaptic device and an optoelectronic synaptic transistor, providing new technological pathways for advanced artificial vision systems and neuromorphic computing vision [1][2] Group 1: Ultraviolet Optoelectronic Synaptic Device - The ultraviolet optoelectronic synaptic device utilizes the ferroelectric polarization characteristics of AlScN and the excellent optoelectronic properties of GaN, successfully constructed based on the trapping and detrapping mechanism of holes at the heterojunction [1] - This device exhibits outstanding non-volatile storage characteristics and can simulate synaptic functions in biological visual systems, enabling multi-state modulation such as long-term potentiation (LTP), paired-pulse facilitation (PPF), and learning-forgetting-relearning processes [1][2] Group 2: Optoelectronic Synaptic Transistor - The optoelectronic synaptic transistor achieves wide-spectrum high optoelectronic conversion efficiency and long data retention capability from ultraviolet to near-infrared through a gas adsorption-assisted persistent photoconductivity strategy [1][2] - It demonstrates excellent photodetection performance across a broad spectrum from 375nm to 1310nm, with a paired-pulse facilitation index reaching 158%, significantly enhancing the precision and efficiency of neural networks in processing visual information [2] Group 3: Implications for Computing and Vision Systems - The development of these devices provides efficient and biomimetic solutions for hardware implementation of multispectral neuromorphic vision systems, simulating the perception and recognition functions of human retinal cells for multispectral signals [2] - Neuromorphic vision systems, which mimic the structure of neurons and synapses in the human brain, can process multiple streams of information simultaneously, significantly reducing power consumption and improving data processing speed compared to traditional serial processing visual systems [2]
“计算机视觉被GPT-4o终结了”(狗头)
量子位· 2025-03-29 07:46
Core Viewpoint - The article discusses the advancements in computer vision (CV) and image generation capabilities brought by the new GPT-4o model, highlighting its potential to disrupt existing tools and methodologies in the field [1][2]. Group 1: Technological Advancements - GPT-4o introduces native multimodal image generation, expanding the functionalities of AI tools beyond traditional applications [2][12]. - The image generation process in GPT-4o is based on a self-regressive model, differing from the diffusion model used in DALL·E, which allows for better adherence to instructions and enhanced image editing capabilities [15][19]. - Observations suggest that the image generation may involve a multi-scale self-regressive combination, where a rough image is generated first, followed by detail filling while the rough shape evolves [17][19]. Group 2: Industry Impact - The advancements in GPT-4o's capabilities have raised concerns among designers and computer vision researchers, indicating a significant shift in the competitive landscape of AI tools [6][10]. - OpenAI's approach of scaling foundational models to achieve these capabilities has surprised many in the industry, suggesting a new trend in AI development [12][19]. - The potential for GPT-4o to enhance applications in autonomous driving has been noted, with implications for future developments in this sector [10]. Group 3: Community Engagement - The article encourages community members to share their experiences and innovative uses of GPT-4o, fostering a collaborative environment for exploring AI applications [26].
等待13年,AlexNet重磅开源:Hinton团队亲手写的原版代码,甚至还带注释
3 6 Ke· 2025-03-24 11:38
Core Insights - AlexNet's original source code has been open-sourced after 13 years, allowing AI developers and deep learning enthusiasts to access the foundational code that revolutionized computer vision [1][10][11] - The release includes the original 2012 version written by Geoffrey Hinton's team, complete with annotations, providing insights into the development process of deep learning models [1][11] Group 1: Historical Context - AlexNet emerged in 2012 during the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), significantly reducing the Top-5 error rate from 26.2% to 15.3%, marking a pivotal moment in computer vision [2][3] - Prior to AlexNet, neural networks faced skepticism and were largely overlooked due to limitations in computational power and data availability, with a resurgence occurring in the 1980s following the rediscovery of the backpropagation algorithm [4][6] Group 2: Technical Aspects - AlexNet consists of 5 convolutional layers and 3 fully connected layers, totaling 60 million parameters and 650,000 neurons, utilizing GPU acceleration for training [2][3] - The success of AlexNet was facilitated by the availability of the ImageNet dataset, which was crowdsourced and became the largest image dataset at the time, and advancements in GPU technology, particularly NVIDIA's CUDA programming system [5][6] Group 3: Development and Impact - The open-sourcing of AlexNet's code was a collaborative effort between the Computer History Museum and Google, taking five years to navigate licensing complexities [10][11] - AlexNet's publication has led to over 170,000 citations, establishing it as a seminal work in the field of deep learning and influencing subsequent research and development in AI [7][10]
AI网红,24小时无休,年入7000万
创业邦· 2025-03-01 09:42
以下文章来源于乌鸦智能说 ,作者智能乌鸦 乌鸦智能说 . 人人都能读懂的AI商业 来 源丨乌鸦智能说(ID:wuyazhinengshuo) 作者丨朗朗 图源丨Midjourney 今天带你们见识下新时代的永动机——AI网红。 她们不会疲惫,永不塌房,还能24小时创造现金流,堪称资本家眼中完美的"打工人"。 当所有人还在摸索用AI赚钱的正确姿势,AI网红率先找到了专属的生态位: 从年入千万美元的顶奢代言,到OnlyFans上的AI分身代聊,再到小红书上AI猫狗博主靠"云养宠"狂揽品 牌商单…… 开发超过70产品的硅谷狂人Levelsio说,"AI网红现在正迅速成为一门大生意"。 预计到2030年,AI网红的市场规模将达到450亿美元。这几乎是现在网红市场的两倍。 各位打工人请系好安全带,今天我们要解剖这个赛博奇观:一群不会呼吸的电子生命体,正在用代码重 构"劳动"的定义。 从时尚博主到AI分身 解锁AI网红赚钱姿势 当大部分AI初创公司还在摸索商业模式,AI网红的变现方式早已五花八门:不仅有品牌代言与联名、情 感经济与订阅服务、还有数字资产衍生产品等等。 接下来,就跟着乌鸦君一起来看看吧。 1、时尚、颜值博主 ...
云天励飞:云天励飞首次公开发行股票并在科创板上市招股意向书
2023-03-15 11:31
Ie 1、n 本次股票发行后拟在科创板市场上市,该市场具有较高的投资风险。科 创板公司具有研发投入大、经营风险高、业绩不稳定、退市风险高等特点, 投资者面临较大的市场风险。投资者应充分了解科创板市场的投资风险及本 公司所披露的风险因素,审慎作出投资决定。 深圳云天励飞技术股份有限公司 (深圳市龙岗区园山街道龙岗大道 8288 号深圳大运软件小镇 36 栋 4 楼) 首次公开发行股票并在科创板上市 招股意向书 保荐人(主承销商) (广东省深圳市福田区中心三路 8 号卓越时代广场(二期)北座) 联席主承销商 (北京市朝阳区安立路 66 号 4 号楼) 副主承销商 (广东省广州市黄埔区中新广州知识城腾飞一街 2 号 618 室) 深圳云天励飞技术股份有限公司 招股意向书 发行人声明 发行人及全体董事、监事、高级管理人员承诺招股意向书及其他信息披露 资料不存在虚假记载、误导性陈述或重大遗漏,并对其真实性、准确性、完整 性承担个别和连带的法律责任。 发行人控股股东、实际控制人承诺本招股意向书不存在虚假记载、误导性 陈述或重大遗漏,并对其真实性、准确性、完整性承担个别和连带的法律责 任。 公司负责人和主管会计工作的负责人 ...