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Talks on
MediaEval 2020
Workshop
Presenter: Koa-Shing Hwang, Professor
EE Dept., NSYSU
EMAILTO: hwang@g-mail.nsysu.edu.tw
Talks @ S. E. College, NWPU
Team: IRIS_NSYSU
01
Method
基本情况简介
点击输入简要文字内容,文字内容需概括精炼,言
简意赅的说明该分项专业设计……输入简要文字内容,
文字内容需概括精炼,言简意赅的说明该分项内容。
THREE
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
基本情况简介
点击输入简要文字内容,文字内容需概括精炼,言
简意赅的说明该分项专业设计……输入简要文字内容,
文字内容需概括精炼,言简意赅的说明该分项内容。
THREE 研究方法
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
基本情况简介
点击输入简要文字内容,文字内容需概括精炼,言
简意赅的说明该分项专业设计……输入简要文字内容,
文字内容需概括精炼,言简意赅的说明该分项内容。
THREE
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
基本情况简介
点击输入简要文字内容,文字内容需概括精炼,言
简意赅的说明该分项专业设计……输入简要文字内容,
文字内容需概括精炼,言简意赅的说明该分项内容。
THREE 研究方法
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
基本情况简介
点击输入简要文字内容,文字内容需概括精炼,言
简意赅的说明该分项专业设计……输入简要文字内容,
文字内容需概括精炼,言简意赅的说明该分项内容。
THREE 研究方法
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
基本情况简介
THREE 研究方法
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
Hybrid Attention Model(HAM)-RPN and Detector
res101
𝑑𝑏𝑎𝑠𝑒 = 1024 𝑑𝑏𝑎𝑠𝑒 = 256
Res101 FPN@P4
Hybrid Attention Model(HAM)-RPN與Detector
res101
𝑑𝑏𝑎𝑠𝑒 = 256
FPN@P4
基本情况简介
THREE Propsoed Method
Conv layer
FC layer
Conv or FC
layer
Local region
proposals
Visited
region
Bounding box
regression
Bounding box
classification
Coordinate of
bounding region
Termination
threshold
RPN
Detector
RoI
Align
Feature
extractor
Feature
maps
reg
HAM
Hybrid Attention Model(HAM)
Hybrid Attention Model(HAM)
X
X
ß-softmax
mask
X X GRU
argmax
Bounding
region
localization
ℎ𝑡−1
(h, w, 𝑑ℎ)
𝑆𝑡
(h, w, 𝑑𝑏𝑎𝑠𝑒)
𝑊
𝑞
𝑊𝑘
𝑊
𝑣
Reinforcement
learning
ℎ𝑡
Attention module
Attention
scores
Masked
attention
scores
Spatial channel (hard attention)
Feature
index i
Temporal channel (soft attention)
X
X
Visited region
Attention
vector
Q
K
V
𝜶𝑖
normalize 𝐏
TR
Coordinate of
bounding region
Termination
threshold
THREE
Hybrid Attention Model(HAM)
X
X
ß-softmax
mask
normalize
X GRU
argmax
Bounding
region
localization
ℎ𝑡−1
(h, w, 𝑑ℎ)
𝑆𝑡
(h, w, 𝑑𝑏𝑎𝑠𝑒)
𝑊
𝑞
𝑊𝑘
𝑊
𝑣
Reinforcement
learning
ℎ𝑡
Attention module
Attention
scores
Spatial channel (hard attention)
Feature
index 𝒊
Temporal channel (soft attention)
X
X
Visited region Attention
vector
Q
K
V
Attention Values
Masked
attention
scores
𝜶𝑖
X
P
TR
Coordinate of
bounding region
Termination
threshold
THREE 研究方法
HAM的輸出動作
• Search termination
• 𝑇𝑡
′
= 𝑡𝑎𝑛ℎ 𝑊𝑇′ ⊗ 𝑟𝑒𝑙𝑢 𝑊ℎ ⊗ ℎ𝑡 + 𝑏ℎ + 𝑏𝑇′
• 𝑇𝑡 = 𝑟𝑒𝑠𝑖𝑧𝑒 𝑇𝑡
′
• 𝑇𝑅 = 𝜎 𝑊𝑇𝑇𝑡
• Region localization
• P = argmax
𝑖
𝒂𝒍𝒑𝒉𝒂𝑖
• 𝑊ℎ ∈ ℛ3×3×𝑑ℎ×128
• 𝑊𝑇′ ∈ ℛ3×3×128×1
• 𝑊𝑇 ∈ ℛ625×1
• 𝑏ℎ ∈ ℛ128
• 𝑏𝑇′ ∈ ℛ1
THREE 研究方法
initialization
t = 1;
𝐼𝑂𝑈𝑖
= 0, 𝐼𝑂𝑈𝑡
𝑖
= 0;
gt_nums set to the number of Ground Truths;
while not Terminal or 𝒕 < 𝒕𝒎𝒂𝒙
if t is not the terminal step
𝑟𝑡
𝑇𝑅
= −
0.05
𝑛𝑢𝑚𝑔𝑡𝑠
; punishment
get 𝐼𝑂𝑈𝑡
𝑖
for all 𝑔𝑖;
if 𝒈𝒊 satisfies 𝑰𝑶𝑼𝒕
𝒊
> 𝑰𝑶𝑼𝒊
and 𝑰𝑶𝑼𝒊
> 𝟎. 𝟓
𝑟𝑡
𝑝
=
1
𝑛𝑢𝑚𝑔𝑡𝑠
𝑖 𝐼𝑂𝑈𝑡
𝑖
− 𝐼𝑂𝑈𝑖
;
end if
else
𝑟𝑡
𝑇𝑅
=
𝑚𝑖𝑛
𝑖
𝐼𝑂𝑈𝑖−0.5
0.5
; reward or punishment
𝑟𝑡
𝑝
= 0;
break
end if
𝐼𝑂𝑈𝑖
= 𝑚𝑎𝑥(𝐼𝑂𝑈𝑖
, 𝐼𝑂𝑈𝑡
𝑖
);
end while
THREE 研究方法
模型訓練
• 𝐽 𝜃𝐻 ≈ − 𝑡=1
𝑡𝑟𝑎𝑗
𝐿𝑝 𝑝𝑡, 𝑝𝑡
∗
𝑅𝑡 + 𝐿𝑇𝑅 𝑎𝑡
𝑇𝑅
, 𝑎𝑡
𝑇𝑅∗
𝑅𝑡
• 𝐿𝑝 𝑝𝑡, 𝑝𝑡
∗
= −𝑝𝑡
∗
𝑙𝑜𝑔 𝑝𝑡
• 𝐿𝑇𝑅 𝑎𝑡
𝑇𝑅
, 𝑎𝑡
𝑇𝑅∗
= −𝑎𝑡
𝑇𝑅∗
𝑙𝑜𝑔 𝑎𝑡
𝑇𝑅
− (1 − 𝑎𝑡
𝑇𝑅∗
) 𝑙𝑜𝑔(1 − 𝑎𝑡
𝑇𝑅
)
• 𝑅𝑡 = 𝐭=0
𝐭𝐫𝐚𝐣
𝛾𝒕
𝑟𝑡+1
𝑝
+ 𝑟𝑡+1
𝑇𝑅
, 𝛾 = 0.95
• 𝑅𝑡 =
)
𝑅𝑡−𝑏𝑎𝑡𝑐ℎ_𝑚𝑒𝑎𝑛(𝑅𝑡
)
𝑏𝑎𝑡𝑐ℎ_𝑠𝑡𝑑(𝑅𝑡
HAM reward function
Detector loss function
• 𝐿 𝜃𝐷 = 𝐿𝑐𝑙𝑠 𝒑𝒄, 𝒄 + 𝑐 ≥ 1 𝐿𝑙𝑜𝑐 𝒃𝒄
, 𝐳𝐭
• 𝐿𝑐𝑙𝑠 𝒑𝒄, 𝒄 = − 𝑙𝑜𝑔 𝐩𝐜𝑐
• 𝐿𝑙𝑜𝑐 𝒃𝒄
, 𝐳𝐭 = 𝑖∈: 𝑥,𝑦,𝑤,ℎ smooth𝐿1 𝐛𝐢
𝐜
− 𝐳𝐭𝐢
• smooth𝐿1 𝑥 =
0.5𝑥2
if 𝑥 < 1
𝑥 − 0.5 otherwise,
THREE 研究方法
利用HAM進行改進的結果
FIN - Talks @
MediaEval 2020
Workshop
Koa-Shing Hwang, Professor
EE Dept., NSYSU
hwang@g-mail.nsysu.edu.tw
Q & A

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A Temporal-Spatial Attention Model for Medical Image Detection

Editor's Notes

  1. 運作流程 輸入圖片 特徵擷取網路作特徵擷取得到特徵圖 RPN在特徵圖上面掃描得到全域RoIs HAM進行順序性搜尋 決定是否停止搜尋(Search termination) 選取特徵圖上的局部區域(Local region localization) 提取局部RoIs (local region proposals, lRoIs) 將lRoIs送到RoIAlign使lRoIs變成相同的尺寸 lRoIs送入偵測器進行分類與邊框迴歸 一步一步 偵測結束後會再統整得到最後的結果 進行NMS得到最後的偵測結果 模組的功能在運作流程中介紹 rpn: 只用來產生全域的RoIs,而不會去篩選最後需要輸入至偵測器的RoIs HAI: 基於混和注意力機制的深度強化學習搜尋網路,可以順序性選取特徵擷取網路輸出的特徵圖上的局部區域,提取由此局部區域的錨框所產生的RoIs (我們稱之為local region proposals, lRoIs) ,如果認為已經偵測結束,再產生結束訊號終止整個程序 偵測器: 將lRoIs作最後的分類與邊框迴歸 特性 不須使用窮盡搜索法,順序性的選擇部分的RoIs 可決定要搜尋之區域、自動停止搜尋 像人類一樣順序性的觀察整張圖片 比全域的物件偵測模型更具合理性
  2. 淺藍色部分是特徵擷取網路,橘色部分是偵測器 RPN與偵測器在GRP-HAI中是負責產生全域的RoIs以及將選取到的lRoIs作最後的分類及邊框迴歸 特徵擷取網路的部分,GRP-HAI可以使用任何一種深度卷積網路架構 因為res101維度大,有時候會無法收斂且計算速度變更久,所以FPN@P4 FPN@P4偵測器的部分則是使用兩個1024-d 全連接層 低層的特徵語意信息較少,但位置訊息準確;高層的特徵語意訊息較豐富,但位置訊息可能會消去
  3. 淺藍色部分是特徵擷取網路,橘色部分是偵測器 RPN與偵測器在GRP-HAI中是負責產生全域的RoIs以及將選取到的lRoIs作最後的分類及邊框迴歸 特徵擷取網路的部分,GRP-HAI可以使用任何一種深度卷積網路架構 因為res101維度大,有時候會無法收斂且計算速度變更久,所以FPN@P4 FPN@P4偵測器的部分則是使用兩個1024-d 全連接層 低層的特徵語意信息較少,但位置訊息準確;高層的特徵語意訊息較豐富,但位置訊息可能會消去