28 papers · ranked by Valyu relevance
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
'Eftychios Protopapadakis'] Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep…
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson
'Stefan Carlsson'] Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available…
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, Timothée Masquelier
'Timothée Masquelier'] Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and…
Tien Ho-Phuoc
Visual object recognition plays an essential role in human daily life. This ability is so efficient that we can recognize a face or an object seemingly without effort, though they may vary in position, scale, pose, and illumination. In the field of computer vision, a large number of studies have been carried out to…
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…
Florentine Klepel, Rainer Goebel
To investigate the relationship of perception and imagery, we model the visual ventral stream with an encoder and decoder part with the help of capsule networks. The proposed network consists of V1 and V2 from CorNet-Z, as well as the Capsule Network architecture with the routing by agreement algorithm for V4 and IT.…
Horea Mureşan, Mihai Oltean
In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.
Soren Goyal, Paul R. Benjamin
Recognition of objects using Deep Neural Networks is an active area of research and many breakthroughs have been made in the last few years. The paper attempts to indicate how far this field has progressed. The paper briefly describes the history of research in Neural Networks and describe several of the recent…
Minju Jung, Jungsik Hwang, Jun Tani, Xuchu Weng
It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most…
Huawei Xu, Ming Liu, Delong Zhang
Using deep neural networks (DNNs) as models to explore the biological brain is controversial, which is mainly due to the impenetrability of DNNs. Inspired by neural style transfer, we circumvented this problem by using deep features that were given a clear meaning—the representation of the semantic content of an image.…
Andrea De Cesarei, Shari Cavicchi, Giampaolo Cristadoro, Marco Lippi
The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the…
Courtney J Spoerer, Tim C Kietzmann, Johannes Mehrer, Ian Charest + 1 more
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a…
Jessica Loke, Noor Seijdel, Lukas Snoek, Matthew van der Meer + 4 more
Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent…
Karim Rajaei, Yalda Mohsenzadeh, Reza Ebrahimpour, Seyed-Mahdi Khaligh-Razavi
Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the…
Alan Yuille, Chenxi Liu
This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the center of recent progress on artificial intelligence and are of growing importance in cognitive science and neuroscience. They have enormous successes but also clear limitations. There is also only partial understanding…
Shakeel Shafiq, Tayyaba Azim, Muhammad Asif
Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling…
Olivia Guest, Bradley C. Love
Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these…
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Deep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often reaching or surpassing human-level accuracy. This success is due in part to the ability of DNNs to learn useful representations of…
M. W. Spratling
Predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article…
Karim Rajaei, Seyed-Mahdi Khaligh-Razavi, Masoud Ghodrati, Reza Ebrahimpour + 2 more
'Reza Ebrahimpour' 'Mohammad Ebrahim Shiri Ahmad Abadi' 'Hans P. Op. de Beeck'] The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically…
Sweta Kumari, V Y Shobha Amala, M Nivethithan, V. Srinivasa Chakravarthy
We propose a biologically inspired attentional search model for target search in a 3D environment, which has two separate channels for object classification, analogous to the “what” pathway in the human visual system, and for prediction of the next location of the camera, analogous to the “where” pathway. We generated…
Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for…
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…
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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…
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The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
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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…
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The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
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…