Attention implements an information-processing bottleneck that allows only a small part of the incoming sensory information to reach short-term memory and visual awareness.
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This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.
Deep learning techniques like convolutional neural networks (CNNs) and deep neural networks have achieved human-level performance on certain tasks. Pioneers in the field include Geoffrey Hinton, who co-invented backpropagation, Yann LeCun who developed CNNs for image recognition, and Andrew Ng who helped apply these techniques at companies like Baidu and Coursera. Deep learning is now widely used for applications such as image recognition, speech recognition, and distinguishing objects like dogs from cats, often outperforming previous machine learning methods.
最近の研究情勢についていくために - Deep Learningを中心に - Hiroshi Fukui
This document summarizes key developments in deep learning for object detection from 2012 onwards. It begins with a timeline showing that 2012 was a turning point, as deep learning achieved record-breaking results in image classification. The document then provides overviews of 250+ contributions relating to object detection frameworks, fundamental problems addressed, evaluation benchmarks and metrics, and state-of-the-art performance. Promising future research directions are also identified.
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
This document provides an overview of variational autoencoders (VAEs) through summaries of three sections:
1. Yann LeCun discusses why unsupervised and predictive learning are important for developing common sense in machines. He argues that generative models allow machines to make accurate predictions by learning the structure of data.
2. Jaan Altosaar's tutorial explains that a VAE can be seen as a denoising autoencoder that learns a probabilistic model. The encoder approximates the posterior distribution and the decoder parameterizes a deep generative model.
3. Shakir Mohamed derives the VAE objective function from importance sampling, showing that it maximizes the likelihood while regularizing the approximate posterior
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Deep neural networks (DNNs) are loosely modeled after the human brain's neurons. DNNs consist of artificial neurons connected in layers that transmit signals from layer to layer, similar to how the brain's neurons receive and transmit signals. While DNNs are inspired by the brain, there are also significant differences in how learning occurs between DNNs, which are trained using backpropagation, and the brain, which learns through neuroplasticity and reinforcement. Emerging areas of AI, like few-shot learning and predictive processing, attempt to further mimic human learning abilities.
Deep neural networks (DNNs) are loosely modeled after the human brain's neurons. DNNs consist of artificial neurons connected in layers that transmit signals from layer to layer, similar to how the brain's neurons receive and transmit signals. While DNNs are inspired by the brain, there are also significant differences in how learning occurs between DNNs, which are trained using backpropagation, and the brain, which uses neuroplasticity to strengthen and weaken connections. Both DNNs and the brain use reinforcement learning and reward prediction errors to learn from experiences.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
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Face recognition using artificial neural networkSumeet Kakani
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Deep learning techniques like convolutional neural networks (CNNs) and deep neural networks have achieved human-level performance on certain tasks. Pioneers in the field include Geoffrey Hinton, who co-invented backpropagation, Yann LeCun who developed CNNs for image recognition, and Andrew Ng who helped apply these techniques at companies like Baidu and Coursera. Deep learning is now widely used for applications such as image recognition, speech recognition, and distinguishing objects like dogs from cats, often outperforming previous machine learning methods.
最近の研究情勢についていくために - Deep Learningを中心に - Hiroshi Fukui
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This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
This document provides an overview of variational autoencoders (VAEs) through summaries of three sections:
1. Yann LeCun discusses why unsupervised and predictive learning are important for developing common sense in machines. He argues that generative models allow machines to make accurate predictions by learning the structure of data.
2. Jaan Altosaar's tutorial explains that a VAE can be seen as a denoising autoencoder that learns a probabilistic model. The encoder approximates the posterior distribution and the decoder parameterizes a deep generative model.
3. Shakir Mohamed derives the VAE objective function from importance sampling, showing that it maximizes the likelihood while regularizing the approximate posterior
Algorithms that mimic the human brain (1)Bindu Reddy
Deep neural networks (DNNs) are loosely modeled after the human brain's neurons. DNNs consist of artificial neurons connected in layers that transmit signals from layer to layer, similar to how the brain's neurons receive and transmit signals. While DNNs are inspired by the brain, there are also significant differences in how learning occurs between DNNs, which are trained using backpropagation, and the brain, which learns through neuroplasticity and reinforcement. Emerging areas of AI, like few-shot learning and predictive processing, attempt to further mimic human learning abilities.
Deep neural networks (DNNs) are loosely modeled after the human brain's neurons. DNNs consist of artificial neurons connected in layers that transmit signals from layer to layer, similar to how the brain's neurons receive and transmit signals. While DNNs are inspired by the brain, there are also significant differences in how learning occurs between DNNs, which are trained using backpropagation, and the brain, which uses neuroplasticity to strengthen and weaken connections. Both DNNs and the brain use reinforcement learning and reward prediction errors to learn from experiences.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
Top object detection algorithms in deep neural networksApuChandraw
This document discusses object detection using deep neural networks. It describes different types of neural networks including convolutional neural networks (CNNs), which are well-suited for object detection tasks. CNNs use techniques like parameter sharing and sparse connections to recognize visual patterns. Popular object detection algorithms that use CNNs are R-CNN, which proposes regions and classifies them, and YOLO (You Only Look Once), which frames detection as a single regression problem from image to bounding boxes. While very fast, YOLO struggles with small or grouped objects and unusual aspect ratios.
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This document summarizes Kevin McGuinness' presentation on deep learning for computer vision. It discusses visual attention models and their ability to predict eye gaze, applications in image cropping, retrieval and classification. It also covers medical image analysis using deep learning for knee osteoarthritis grading and neonatal brain segmentation. Deep crowd analysis is examined for crowd counting. Finally, interactive deep vision for image segmentation using user interactions is presented.
Deep learning for pose-invariant face detection in unconstrained environmentIJECEIAES
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This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
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This document summarizes recent advances in real-time object detection using deep learning. It first provides an overview of object detection and deep learning. It then reviews popular object detection models including CNNs, R-CNNs, Fast R-CNN, Faster R-CNN, YOLO, and SSD. The document proposes modifications to existing models to improve small object detection accuracy. Specifically, it proposes using Darknet-53 with feature map upsampling and concatenation at multiple scales to detect objects of different sizes. It also describes using k-means clustering to select anchor boxes tailored to each detection scale.
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Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
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and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
This is an introduction to deep learning presented to Plymouth University students. In the introduction it is explained how a neural network works. In the practical section it is shown how to use Tensorflow for building simple models. Finally the case studies, how to use deep learning in real world applications.
- Researchers used a hierarchical convolutional neural network (CNN) optimized for object categorization performance to predict neural responses in higher visual cortex.
- The top layer of the CNN accurately predicted responses in inferior temporal (IT) cortex, and intermediate layers predicted responses in V4 cortex.
- This suggests that biological performance optimization directly shaped neural mechanisms in visual processing areas, as the CNN was not explicitly trained on neural data but emerged as predictive of responses in IT and V4.
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The document discusses simulating human-like attention using a biologically realistic model. It describes creating a program that simulates neuronal competition and suppression in the visual cortex using linear and grid connections between neurons. The program is able to replicate behaviors observed in biological models, such as edge effects and contrast enhancement at borders. While a bottom-up approach can simulate basic attention mechanisms, fully simulating the human brain is extremely computationally expensive and many neuronal details remain unknown.
The document discusses an unsupervised learning algorithm that learns visual representations from natural images and videos. When applied to images, the algorithm learns retinal ganglion cell properties, and when applied to sounds, it learns auditory nerve properties. The algorithm is also used to learn hierarchical representations in a model called RICA, which learns simple cell properties in early visual areas. RICA can be used for face and object recognition tasks.
The document discusses an unsupervised learning algorithm that learns visual representations from natural images and videos. When applied to images, the algorithm learns retinal ganglion cell properties, and when applied to sounds, it learns auditory nerve properties. The algorithm is also used to learn hierarchical representations in a model called RICA, which learns simple cell properties in early visual areas. RICA can be used for face and object recognition tasks.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
This document discusses one-shot learning techniques for object recognition from few examples. It introduces the concepts of embedding spaces and similarity metrics for measuring distances between objects. Specific deep learning models are described, including Siamese networks, triplet networks, DeepFace, and FaceNet. Siamese networks aim to learn a similarity function using a contrastive loss over input pairs, while triplet networks employ a triplet loss to optimize relative distances between anchor, positive, and negative examples. DeepFace and FaceNet are state-of-the-art face recognition systems that use deep convolutional networks trained with triplet losses to learn embeddings that achieve human-level accuracy on benchmark face datasets.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Quality Assurance in Modern Software DevelopmentZahra Sadeghi
This document discusses quality assurance in modern software development. It begins by providing resources on the topic and outlining the agenda. It then reviews basic concepts of software, quality, and the differences between quality assurance and quality control. It introduces several quality models including McCall's quality model and discusses important factors in software quality. Finally, it covers quality assurance methodology using PDCA, quality management tools including Ishikawa diagrams and Pareto charts, and software quality testing. The document provides a comprehensive overview of key aspects of quality assurance in software development.
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3) A word generator used to input 16-bit digital words or patterns into a circuit in parallel.
4) A logic analyzer that displays the signal levels of up to 16 lines in a circuit over time.
5) Other tools include a multimeter to measure voltage, current and resistance
The document describes the MS-DOS boot process. It begins with the CPU initialization and BIOS checks like the POST. The BIOS then looks to the MBR and loads the boot code. This boot code looks for the IO.SYS and MSDOS.SYS files to load the kernel. Next CONFIG.SYS is read to configure devices. COMMAND.COM is then loaded, which looks for the AUTOEXEC.BAT file. Finally, the command prompt is displayed.
This document provides an introduction to threads. It discusses the history of threads, key terminology like processes and threads, and how threads allow programs to perform multiple tasks concurrently. The document also covers benefits of threads like improving responsiveness, but notes costs like reduced processor time per thread. It provides examples of how threads work and challenges like race conditions that can occur with shared memory access across threads.
Multimedia involves using multiple media types like text, graphics, sound, and animation together. It is often used to refer to including audio, video, and animation on web pages. There are three main categories of multimedia applications: streaming stored audio and video, streaming live audio/video, and real-time interactive audio/video. Streaming stored media involves compressing and storing files on a web or streaming server. Users can pause, resume, and skip around files as they download. Streaming live media is similar to radio and TV broadcasts over the internet. Real-time interactive media allows people to communicate with audio and video in real-time, like internet telephony. Common compression techniques compress audio by encoding differences between samples and compress video
This document discusses various penalty function methods for handling constraints in genetic algorithms. It describes 4 categories of constraint handling methods:
1. Methods based on penalty functions, including death penalty, static penalties, dynamic penalties, annealing penalties, adaptive penalties, segregated genetic algorithms, and co-evolutionary penalties.
2. Methods based on searching the feasible solution space directly, including repairing unfeasible solutions, preferring feasible solutions, and using behavioral memory.
3. Methods aimed at preserving feasibility, such as the GENOCOP system, boundary searching, and homomorphous mapping.
4. Hybrid methods that combine multiple constraint handling approaches.
It then provides more details on several specific penalty function
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
2. Overview
• Attention
• Visual saliency
• Bottom-up attention
• Koch-Ulman framework
• Visual Attention in brain
• Coarse to Fine theory
• Top-Down Facilitation
• Comparing Attentional Neural Network with human behavior
2
3. Attention
• Attention implements an information-processing bottleneck that
allows only a small part of the incoming sensory information to reach
short-term memory and visual awareness.
• key challenge is to select which impressions are relevant and which
inputs should be ignored.
• This process of selecting a subset of the input, and ignoring the rest, is
referred to as attention
• bottom-up and top-down attention, or stimulus-driven and goal-
oriented attention
3
4. Visual saliency
• At a pre-attentive stage some parts
of the scene may pop out.
• Visual saliency refers to the idea that
certain parts of a scene are pre-
attentively distinctive and create
some form of immediate significant
visual arousal
• how can a machine vision system
extract the salient regions from an
unknown background?
4
5. 1. low level feature
extraction
2. Saliency map
creation
3. Winner-Take-All
(WTA)
4. Inhibition of Return
(IoR)
5. Top-down attentional
bias
Flow diagram of a typical model for the control of attention
5
10. Saliency map construction
1- Cross-scaling sum on all created feature channels
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11. Segmentation
• Threshold segmentation (the saliency map is converted into a binary
image using a threshold)
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12. • The ventral (’what’) stream processes visual
shape appearance and is largely responsible
for object recognition.
• The dorsal (’where’) stream encodes spatial
locations and processes motion information.
• Bottom-up information that can guide
attention propagates thus from the visual
cortex to the PFC.
• PFC areas can provide top-down signals to
control attention to some degree
How does the brain process attention?
12
13. • coarse, low spatial frequency (LSF)
information is processed first
• quickly projects from primary visual
cortex to higher level visual areas (PFC, OFC)
• Psychophysical and single-unit recordings in monkeys
indicate that low spatial frequencies are extracted from
scenes earlier than high spatial frequencies
13
14. 14
• We trained a 3 layer deep belief
network and performed an
unsupervised learning scheme
on the obtained deep
representations.
Developmental learning in DNNs: Fine to coarse development
• There’s a progression in
depth in hidden layers of
DBN where low level layers
represent finer distinctions
and high level layers
represent coarser
distinctions
Sadeghi, Zahra. "Deep learning and developmental learning: emergence of fine-to-coarse conceptual categories at layers of deep belief
network." Perception 45.9 (2016): 1036-1045.
15. • Input to the visual system is often noisy and ambiguous
• a growing body of theoretical work and empirical evidence support the idea
that visual recognition is facilitated by top-down expectations
• Context facilitates the recognition of related objects even if these objects are
ambiguous when seen in isolation
• an ambiguous object becomes recognizable if another object that shares the
same context is placed in an appropriate spatial relation to it.
15
Top-down processing contribution
16. + +
inconsistent case consistent case
Effect of context in occluded object recognition
500 ms 500 ms
'Type the name of the object and then press enter’
300 ms
300 ms
16
Sadeghi, Zahra. "The effect of top-down attention in occluded object recognition." arXiv preprint arXiv:2007.10232 (2020).
18. 18
Hit const vs
hit inconst
Miss const vs
miss inconst
Sup hit const vs
sup hit inconst
Sup miss const vs
sup miss inconst
Hypo_pos1 vs
hypo_neg1
Hypo_pos2 vs
hypo_neg2
Resp-time
const vs
inconst
p-val 0.0027 0.0027 0.0027 0.0027 0.0027 0.0027 4.6921e-11
Sadeghi, Zahra. "The effect of top-down attention in occluded object recognition." arXiv preprint arXiv:2007.10232 (2020).
20. Global-And-Local-Attention (GALA)
• Global-and-Local-attention (GALA) network extends the squeeze-and-
excitation (SE) network by adding a local saliency module.
• The attention mechanism is embedded in the cost function as a
regularization term
20
21. • three cases are considered:
1- networks trained on color images and tested on color images.
2- networks trained on grayscale image and tested on grayscale images.
3- networks trained on color images and tested on grayscale image.
the best performance in both color and grayscale
cases is achieved by gala click, while gala no click
and no gala no click obtained second and third best
results respectively.
the highest accuracy for all models is attributed to
the case in which images are trained on colorful
images and tested on colorful images.
21
Sadeghi, Zahra. "An Investigation on Performance of Attention Deep Neural Networks in Rapid Object
Recognition." Intelligent Computing Systems: Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates,
March 18–19, 2020, Proceedings 3. Springer International Publishing, 2020.
22. • to test the effect of importance maps
collected in clickme.ai experiment, a rapid
object recognition experiment was
designed
• The dataset contains 100 images from
animal and non-animal.
• Phase scrambled masks are applied to
images
• eleven versions of each image ordered
ascendingly based on their level of pixel
revelation of important pixels.
22
23. Model and human performance
• Average accuracy of the two gala models
(gala-click and gala no-click) and ResNet-50
model (no-gala-no-click) is compared on
the behavioral test images at different
levels of pixel revelation.
• gala click and gala no click models
achieved similar accuracy.
• gala click model produces superior results
compared to all other models in full pixel
revelation.
• The second best performance in full level,
is achieved by gala-no-click model.
23
Sadeghi, Zahra. "An Investigation on Performance of Attention Deep Neural Networks in Rapid Object
Recognition." Intelligent Computing Systems: Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates,
March 18–19, 2020, Proceedings 3. Springer International Publishing, 2020.
24. • Human visual attention is well-studied
• while there exist different models, they lack computational efficacy of
our visual system
• Attention Mechanisms in Neural Networks are still loosely based on
the visual attention mechanism found in humans.
24