本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This paper presents a design and implementation of FPGA based Bose, Chaudhuri and Hocquenghem (BCH) codes for wireless communication applications. The codes are written in VHDL (Very High Speed Hardware Description Language). Here BCH decoder (15, 5, and 3) is implemented and discussed. And decoder uses serial input and serial output architecture. BCH code forms a large class of powerful random error correcting cyclic codes. BCH operates over algebraic structure called finite fields and they are binary multiple error correcting codes. BCH decoder is implemented by syndrome calculation circuit, the BMA (Berlekamp-Massey algorithm) and Chien search circuit. The codecs are implemented over cyclone FPGA device.
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This paper presents a design and implementation of FPGA based Bose, Chaudhuri and Hocquenghem (BCH) codes for wireless communication applications. The codes are written in VHDL (Very High Speed Hardware Description Language). Here BCH decoder (15, 5, and 3) is implemented and discussed. And decoder uses serial input and serial output architecture. BCH code forms a large class of powerful random error correcting cyclic codes. BCH operates over algebraic structure called finite fields and they are binary multiple error correcting codes. BCH decoder is implemented by syndrome calculation circuit, the BMA (Berlekamp-Massey algorithm) and Chien search circuit. The codecs are implemented over cyclone FPGA device.
On Optimization of Network-coded Scalable Multimedia Service MulticastingAndrea Tassi
In the near future, the delivery of multimedia multicast services over next-generation networks is likely to become one of the main pillars of future cellular networks. In this extended abstract, we address the issue of efficiently multicasting layered video services by defining a novel optimization paradigm that is based on an Unequal Error Protection implementation of Random Linear Network Coding, and aims to ensure target service coverages by using a limited amount of radio resources.
Fast Object Recognition from 3D Depth Data with Extreme Learning MachineSoma Boubou
Object recognition from RGB-D sensors has recently emerged as a renowned and challenging research topic. The current systems often require large amounts of time to train the models and to classify new data. We proposed an effective and fast object recognition approach from 3D data acquired from depth sensors such as Structure or Kinect sensors.
Our contribution in this work} is to present a novel fast and effective approach for real-time object recognition from 3D depth data:
- First, we extract simple but effective frame-level features, which we name as differential frames, from the raw depth data.
- Second, we build a recognition system based on Extreme Learning Machine classifier with a Local Receptive Field (ELM-LRF).
Injecting image priors into Learnable Compressive SubsamplingMartino Ferrari
My master thesis work extends the problem formulation of learnable compressive subsampling [1] that focuses on the learning of the best sampling operator in the Fourier domain adapted to spectral properties of a training set of images. I formulated the problem as a reconstruction from a finite number of sparse samples with a prior learned from the external dataset or learned on-fly from the images to be reconstructed. More in
details, I developed two very different methods, one using multiband coding in the spectral domain and the second using a neural network.
The new methods can be applied to many different fields of spectroscopy and Fourier optics, for example in medical (computerized tomography, magnetic resonance spectroscopy) and astronomy (the Square Kilometre Array) imaging, where the capability to reconstruct high-quality images, in the pixel domain, from a limited number of samples, in the frequency domain, is a key issue.
The proposed methods have been tested on diverse datasets covering facial images, medical and multi-band astronomical data, using the mean square error and SSIM as a perceptual measure of the quality of the reconstruction.
Finally, I explored the possible application in data acquisition systems such as computer tomography and radio astronomy. The obtained results demostrate that the properties of the proposed methods have a very promising potential for future research and extensions.
For such reason, the work was both presented at the poster session of the EUSIPCO 2018 conference in Rome and submitted for a EU patent.
[1] L. Baldassarre, Y.-H. Li, J. Scarlett, B. Gözcü, I. Bogunovic, and V.
Cevher, “Learning-based compressive subsampling,” IEEE Journal of Selected
Topics in Signal Processing, vol. 10, no. 4, pp. 809–822, 2016
TWO DIMENSIONAL MODELING OF NONUNIFORMLY DOPED MESFET UNDER ILLUMINATIONVLSICS Design
A two dimensional numerical model of an optically gated GaAs MESFET with non uniform channel doping has been developed. This is done to characterize the device as a photo detector. First photo induced voltage (Vop) at the Schottky gate is calculated for estimating the channel profile. Then Poisson’s equation for the device is solved numerically under dark and illumination condition. The paper aims at developing the MESFET 2-D model under illumination using Monte Carlo Finite Difference method. The results discuss about the optical potential developed in the device, variation of channel potential under different biasing and illumination and also about electric fields along X and Y directions. The Cgs under different illumination is also calculated. It has been observed from the results that the characteristics of the device are strongly influenced by the incident optical illumination.
COMPARATIVE STUDY ON BENDING LOSS BETWEEN DIFFERENT S-SHAPED WAVEGUIDE BENDS ...cscpconf
Bending loss in the waveguide as well as the leakage losses and absorption losses along with a comparative study among different types of S-shaped bend structures has been computed with
the help of a simple matrix method.This method needs simple 2×2 matrix multiplication. The
effective-index profile of the bended waveguide is then transformed to an equivalent straight
waveguide with the help of a suitable mapping technique and is partitioned into large number of thin sections of different refractive indices. The transfer matrix of the two adjacent layers will be a 2×2 matrix relating the field components in adjacent layers. The total transfer matrix is
obtained through multiplication of all these transfer matrices. The excitation efficiency of the
wave in the guiding layer shows a Lorentzian profile. The power attenuation coefficient of the
bent waveguide is the full-width-half-maximum (FWHM) of this peak .Now the transition losses and pure bending losses can be computed from these FWHM datas.The computation technique
is quite fast and it is applicable for any waveguide having different parameters and wavelength of light for both polarizations(TE and TM)
Two Dimensional Modeling of Nonuniformly Doped MESFET Under IlluminationVLSICS Design
A two dimensional numerical model of an optically gated GaAs MESFET with non uniform channel doping has been developed. This is done to characterize the device as a photo detector. First photo induced voltage (Vop) at the Schottky gate is calculated for estimating the channel profile. Then Poisson’s equation for the device is solved numerically under dark and illumination condition. The paper aims at developing the MESFET 2-D model under illumination using Monte Carlo Finite Difference method. The results discuss about the optical potential developed in the device, variation of channel potential under different biasing and illumination and also about electric fields along X and Y directions. The Cgs under different illumination is also calculated. It has been observed from the results that the characteristics of the device are strongly influenced by the incident optical illumination.
https://telecombcn-dl.github.io/idl-2020/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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AUTHOR(S): LEARNING OF OCCLUSION-AWARE ATTENTION FOR PEDESTRIAN DE
tion, outputting the classification scores using global average pooling or global max p
from the feature map f (·). However, global average pooling increases in the respons
of entire feature map at specific class due to using an average of all pixel at a featur
On the other hand, global max pooling does not increase the entire feature map at s
class because of using a maximum pixel value in a feature map. Response score fo
class of global average pooling and global max pooling is calculated as follow Eq. (1
vc
i =
1
M×N ∑M
m=1 ∑N
n=1 fc
m,n (xi) (global average pooling),
max fc
m,n (xi) (global max pooling),
After outputting the score for each class, the attention of pedestrian and occlusion r
are generated. First, we fuse the multiple channel feature map to one channel. In this
we validate the three type fusion as follows in fig. 1(b)∼(d): 1) standard fusion, 2) so
weighting fusion, and 3) squeeze-and-excitation (SE) block fusion. Standard fusion is
summation of feature map. In softmax weighting, it is weighted the feature maps fo
channel using softmax score by Eq. (2). The softmax weighting can mask the unnec
channel feature map. In SE block fusion, it is weighted the feature maps for each c
using the attention of SE block like Squeeze-and-Excitation Network. After fusing
channel, pedestrian classification and occlusion state attentions are fused. In this wo
calculate the attention by subtracting the occlusion attention from pedestrian classifi
attention. Here, we call the attention the attention map because of containing positi
negative values.
Attentioni =
C
∑
c=1
fc
(xi)∗
exp(vc
i )
∑J
j=1 exp vj
i
3.4 Perception branch
In the perception branch, it outputs the final result score using attention map and featu
of RoI pooling. Attention map can refine the feature map of RoI pooling, such as m
unnecessary background feature and enhancing the important locations. Converted
map is made of the inner product of attention map and feature map from RoI poolin
perception branch is composed two fully connected layers like Fast R-CNN. The struc
the perception branch is the same as conventional Fast R-CNN, however, our model e
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Anonymous CVPR submission
Paper ID ****
Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
v1
i , v2
i , v3
i , vc
i (3)
f(xi) (4)
f (xi, yi) (5)
2. Concolusion
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Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
v1
i , v2
i , v3
i , vc
i (3)
C (4)
f (xi, yi) (5)
2. Concolusion
References
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Paper ID ****
Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
v1
i , v2
i , v3
i , vc
i (3)
C (4)
f (xi, yi) (5)
2. Concolusion
References
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Paper ID ****
Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
v1
i , v2
i , v3
i , vc
i (3)
C (4)
f (xi, yi) (5)
2. Concolusion
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vc
i =
1
M×N ∑M
m=1 ∑N
n=1 fc
m,n (xi) (global average pooling),
max fc
m,n (xi) (global max pooling),
(1)
After outputting the score for each class, the attention of pedestrian and occlusion regions
are generated. First, we fuse the multiple channel feature map to one channel. In this work,
we validate the three type fusion as follows in fig. 1(b)∼(d): 1) standard fusion, 2) softmax-
weighting fusion, and 3) squeeze-and-excitation (SE) block fusion. Standard fusion is simply
summation of feature map. In softmax weighting, it is weighted the feature maps for each
channel using softmax score by Eq. (2). The softmax weighting can mask the unnecessary
channel feature map. In SE block fusion, it is weighted the feature maps for each channel
using the attention of SE block like Squeeze-and-Excitation Network. After fusing to one
channel, pedestrian classification and occlusion state attentions are fused. In this work, we
calculate the attention by subtracting the occlusion attention from pedestrian classification
attention. Here, we call the attention the attention map because of containing positive and
negative values.
Attentioni =
C
∑
c=1
fc
(xi)∗
exp(vc
i )
∑J
j=1 exp vj
i
(2)
3.4 Perception branch
In the perception branch, it outputs the final result score using attention map and feature map
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AUTHOR(S): LEARNING OF OCCLUSION-AWARE ATTENTION FOR PEDESTRIAN DETECTION5
tion, outputting the classification scores using global average pooling or global max pooling
from the feature map f (·). However, global average pooling increases in the response value
of entire feature map at specific class due to using an average of all pixel at a feature map.
On the other hand, global max pooling does not increase the entire feature map at specific
class because of using a maximum pixel value in a feature map. Response score for each
class of global average pooling and global max pooling is calculated as follow Eq. (1).
vc
i =
1
M×N ∑M
m=1 ∑N
n=1 fc
m,n (xi) (global average pooling),
max fc
m,n (xi) (global max pooling),
(1)
After outputting the score for each class, the attention of pedestrian and occlusion regions
are generated. First, we fuse the multiple channel feature map to one channel. In this work,
we validate the three type fusion as follows in fig. 1(b)∼(d): 1) standard fusion, 2) softmax-
weighting fusion, and 3) squeeze-and-excitation (SE) block fusion. Standard fusion is simply
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AUTHOR(S): LEARNING OF OCCLUSION-AWARE ATTENTION FOR PEDESTRIAN DETECTION5
tion, outputting the classification scores using global average pooling or global max pooling
from the feature map f (·). However, global average pooling increases in the response value
of entire feature map at specific class due to using an average of all pixel at a feature map.
On the other hand, global max pooling does not increase the entire feature map at specific
class because of using a maximum pixel value in a feature map. Response score for each
class of global average pooling and global max pooling is calculated as follow Eq. (1).
vc
i =
1
M×N ∑M
m=1 ∑N
n=1 fc
m,n (xi) (global average pooling),
max fc
m,n (xi) (global max pooling),
(1)
After outputting the score for each class, the attention of pedestrian and occlusion regions
are generated. First, we fuse the multiple channel feature map to one channel. In this work,
we validate the three type fusion as follows in fig. 1(b)∼(d): 1) standard fusion, 2) softmax-
weighting fusion, and 3) squeeze-and-excitation (SE) block fusion. Standard fusion is simply
summation of feature map. In softmax weighting, it is weighted the feature maps for each
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Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
v1
i , v2
i , v3
i , vc
i (3)
f(xi) (4)
f (xi, yi) (5)
2. Concolusion
References
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How Small Network Can Detect Ped
Anonymous CVPR submission
Paper ID ****
Abstract
1. Introduction
t log y + (1 − t) log (1 − y) (1)
vc
i =
1
M × N
M
m=1
N
n=1
fc
m,n(xi) (2)
M, N (3)
C (4)
6. Table 1. Classification error on the ILSVRC validation set.
Networks top-1 val. error top-5 val. error
VGGnet-GAP 33.4 12.2
GoogLeNet-GAP 35.0 13.2
AlexNet∗-GAP 44.9 20.9
AlexNet-GAP 51.1 26.3
GoogLeNet 31.9 11.3
VGGnet 31.2 11.4
AlexNet 42.6 19.5
NIN 41.9 19.6
GoogLeNet-GMP 35.6 13.9
Table 2. Localization error on the ILSVRC validation set. Bac
prop refers to using [23] for localization instead of CAM.
Method top-1 val.error top-5 val. error
GoogLeNet-GAP 56.40 43.00
VGGnet-GAP 57.20 45.14
GoogLeNet 60.09 49.34
AlexNet∗-GAP 63.75 49.53
AlexNet-GAP 67.19 52.16
NIN 65.47 54.19
Backprop on GoogLeNet 61.31 50.55
13. irshick2
Piotr Doll´ar2
Zhuowen Tu1
Kaiming He2
C San Diego 2
Facebook AI Research
@ucsd.edu {rbg,pdollar,kaiminghe}@fb.com
rized network archi-
etwork is constructed
egates a set of trans-
ur simple design re-
architecture that has
is strategy exposes a
ality” (the size of the
factor in addition to
On the ImageNet-1K
under the restricted
ncreasing cardinality
racy. Moreover, in-
han going deeper or
Our models, named
entry to the ILSVRC
secured 2nd place.
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
+
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
....
total 32
paths
256-d in
+
256, 1x1, 64
64, 3x3, 64
64, 1x1, 256
+
256-d in
256-d out
256-d out
Figure 1. Left: A block of ResNet [14]. Right: A block of
ResNeXt with cardinality = 32, with roughly the same complex-
ity. A layer is shown as (# in channels, filter size, # out channels).
ing blocks of the same shape. This strategy is inherited
by ResNets [14] which stack modules of the same topol-
ogy. This simple rule reduces the free choices of hyper-
parameters, and depth is exposed as an essential dimension
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie1
Ross Girshick2
Piotr Doll´ar2
Zhuowen Tu1
Kaiming He2
1
UC San Diego 2
Facebook AI Research
{s9xie,ztu}@ucsd.edu {rbg,pdollar,kaiminghe}@fb.com
Abstract
We present a simple, highly modularized network archi-
tecture for image classification. Our network is constructed
by repeating a building block that aggregates a set of trans-
formations with the same topology. Our simple design re-
sults in a homogeneous, multi-branch architecture that has
only a few hyper-parameters to set. This strategy exposes a
new dimension, which we call “cardinality” (the size of the
set of transformations), as an essential factor in addition to
the dimensions of depth and width. On the ImageNet-1K
dataset, we empirically show that even under the restricted
condition of maintaining complexity, increasing cardinality
is able to improve classification accuracy. Moreover, in-
creasing cardinality is more effective than going deeper or
wider when we increase the capacity. Our models, named
ResNeXt, are the foundations of our entry to the ILSVRC
2016 classification task in which we secured 2nd place.
We further investigate ResNeXt on an ImageNet-5K set and
the COCO detection set, also showing better results than
its ResNet counterpart. The code and models are publicly
available online1
.
1. Introduction
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
+
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
256, 1x1, 4
4, 3x3, 4
4, 1x1, 256
....
total 32
paths
256-d in
+
256, 1x1, 64
64, 3x3, 64
64, 1x1, 256
+
256-d in
256-d out
256-d out
Figure 1. Left: A block of ResNet [14]. Right: A block of
ResNeXt with cardinality = 32, with roughly the same complex-
ity. A layer is shown as (# in channels, filter size, # out channels).
ing blocks of the same shape. This strategy is inherited
by ResNets [14] which stack modules of the same topol-
ogy. This simple rule reduces the free choices of hyper-
parameters, and depth is exposed as an essential dimension
in neural networks. Moreover, we argue that the simplicity
of this rule may reduce the risk of over-adapting the hyper-
parameters to a specific dataset. The robustness of VGG-
nets and ResNets has been proven by various visual recog-
nition tasks [7, 10, 9, 28, 31, 14] and by non-visual tasks
involving speech [42, 30] and language [4, 41, 20].
Unlike VGG-nets, the family of Inception models [38,
17, 39, 37] have demonstrated that carefully designed
v:1611.05431v2[cs.CV]11Apr2017
Densely Connected Convolutional Networks
Gao Huang⇤
Cornell University
gh349@cornell.edu
Zhuang Liu⇤
Tsinghua University
liuzhuang13@mails.tsinghua.edu.cn
Laurens van der Maaten
Facebook AI Research
lvdmaaten@fb.com
Kilian Q. Weinberger
Cornell University
kqw4@cornell.edu
Abstract
Recent work has shown that convolutional networks can
be substantially deeper, more accurate, and efficient to train
if they contain shorter connections between layers close to
the input and those close to the output. In this paper, we
embrace this observation and introduce the Dense Convo-
lutional Network (DenseNet), which connects each layer
to every other layer in a feed-forward fashion. Whereas
traditional convolutional networks with L layers have L
connections—one between each layer and its subsequent
layer—our network has L(L+1)
2 direct connections. For
each layer, the feature-maps of all preceding layers are
used as inputs, and its own feature-maps are used as inputs
into all subsequent layers. DenseNets have several com-
pelling advantages: they alleviate the vanishing-gradient
problem, strengthen feature propagation, encourage fea-
ture reuse, and substantially reduce the number of parame-
ters. We evaluate our proposed architecture on four highly
competitive object recognition benchmark tasks (CIFAR-10,
CIFAR-100, SVHN, and ImageNet). DenseNets obtain sig-
nificant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high per-
formance. Code and pre-trained models are available at
https://github.com/liuzhuang13/DenseNet.
1. Introduction
Convolutional neural networks (CNNs) have become
the dominant machine learning approach for visual object
recognition. Although they were originally introduced over
20 years ago [18], improvements in computer hardware and
network structure have enabled the training of truly deep
CNNs only recently. The original LeNet5 [19] consisted of
5 layers, VGG featured 19 [29], and only last year Highway
⇤Authors contributed equally
x0
x1
H1
x2
H2
H3
H4
x3
x4
Figure 1: A 5-layer dense block with a growth rate of k = 4.
Each layer takes all preceding feature-maps as input.
Networks [34] and Residual Networks (ResNets) [11] have
surpassed the 100-layer barrier.
As CNNs become increasingly deep, a new research
problem emerges: as information about the input or gra-
dient passes through many layers, it can vanish and “wash
out” by the time it reaches the end (or beginning) of the
network. Many recent publications address this or related
problems. ResNets [11] and Highway Networks [34] by-
pass signal from one layer to the next via identity connec-
tions. Stochastic depth [13] shortens ResNets by randomly
dropping layers during training to allow better information
and gradient flow. FractalNets [17] repeatedly combine sev-
eral parallel layer sequences with different number of con-
volutional blocks to obtain a large nominal depth, while
maintaining many short paths in the network. Although
these different approaches vary in network topology and
training procedure, they all share a key characteristic: they
create short paths from early layers to later layers.
1
arXiv:1608.06993v5[cs.CV]28Jan2018