24 papers · ranked by Valyu relevance
Authors not listed
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as…
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, Lihi Zelnik‐Manor
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequently for pretraining, mainly due to its complexity, low accessibility, and underestimation of its added value. This paper aims to close…
Olawale Salaudeen, Moritz Hardt
We introduce ImageNot, a dataset designed to match the scale of ImageNet while differing drastically in other aspects. We show that key model architectures developed for ImageNet over the years rank identically when trained and evaluated on ImageNot to how they rank on ImageNet. This is true when training models from…
Nizar Massouh
To successfully learn about an object using millions of images requires a model that can handle the magnitude of the task. Convolutional Neural Networks (CNNs) have what it takes to handle the complexity of the object recognition task since they can be built and tuned to accommodate the millions parameters required.…
Chen-Ping Yu, Huidong Liu, Dimitris Samaras, Gregory Zelinsky
Recently we proposed that people represent object categories using category-consistent features (CCFs), those features that occur both frequently and consistently across a categorys exemplars [70]. Here we designed a Convolutional Neural Network (CNN) after the primate ventral stream (VsNet) and used it to extract CCFs…
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause + 8 more
'Sanjeev Satheesh' 'Sean Ma' 'Zhiheng Huang' 'Andrej Karpathy' 'Aditya Khosla' 'Michael S. Bernstein' 'Alexander C. Berg' 'Li Fei-Fei'] O. Russakovsky Stanford University, Stanford, CA, USA E-mail: olga@cs.stanford.edu J. Deng University of Michigan, Ann Arbor, MI, USA (* = authors contributed equally) H. Su Stanford…
Xueyan Mei, Zelong Liu, Philip M. Robson, Brett Marinelli + 11 more
'Mingqian Huang' 'Amish Doshi' 'Adam Jacobi' 'Chendi Cao' 'Katherine E. Link' 'Thomas Yang' 'Ying Wang' 'Hayit Greenspan' 'Timothy Deyer' 'Zahi A. Fayad' 'Yang Yang'] Purpose To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical…
Farhana Sultana, Abu Sufian, Paramartha Dutta
—Convolutional Neural Network (CNN) is the stateof-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet…
Maad Ebrahim, Mohammad Alsmirat, Mahmoud Al-Ayyoub
Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to…
Priyal Sobti, Anand Nayyar, Niharika, Preeti Nagrath + 1 more
Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from…
Martin N. Hebart, Adam H. Dickter, Alexis Kidder, Wan Y. Kwok + 3 more
In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of…
Ana Sofia Cardoso, Francesco Renna, Ricardo Moreno-Llorca, Domingo Alcaraz-Segura + 3 more
Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We…
Hojin Jang, Devin McCormack, Frank Tong
Deep neural networks (DNNs) can accurately recognize objects in clear viewing conditions, leading to claims that they have attained or surpassed human-level performance. However, standard DNNs are severely impaired at recognizing objects in visual noise, whereas human vision remains robust. We developed a…
Bagher Sistaninejhad, Habib Rasi, Parisa Nayeri
Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis…
Yufeng Zheng, Jun Huang, Tianwen Chen, Yang Ou + 1 more
The convolutional neural networks (CNNs) are a powerful tool of image classification that has been widely adopted in applications of automated scene segmentation and identification. However, the mechanisms underlying CNN image classification remain to be elucidated. In this study, we developed a new approach to address…
Niklas Müller, Cees G. M. Snoek, Iris I. A. Groen, H. Steven Scholte
Convolutional Neural Networks (CNNs) surpass human-level performance on visual object recognition and detection, but their behavior still differs from human behavior in important ways. One prominent example is that CNNs trained on ImageNet exhibit an image texture bias, while humans exhibit a strong bias toward object…
Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny + 1 more
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Authors not listed
Determining complete atomic structures directly from microscopy images remains a longstanding challenge in materials science. MicroscopyGPT is a vision-language model (VLM) that leverages multimodal generative pre-trained transformers to predict full atomic configurations including lattice parameters, element types…
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks…
Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu + 2 more
'Nikita Prabhu' 'Srinivas S S Kruthiventi' 'R. Venkatesh Babu'] Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of…
Authors not listed
Infrared (IR) spectroscopy provides rich structural information but interpreting spectra at scale remains challenging. Here we introduce j-IR-vis, a vision-based neural model that learns chemically interpretable representations directly from IR spectra for functional-group prediction and downstream molecular…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…