AAAI2020:

Main concerns:

  1. Generation of bounding boxes from contour is not clear
  2. No info about neighboring ROIs in linking module
  3. FrRCNN+FPN+RA is better than PlotNet in most classes

 

CVPR2020:

Changes: AAAI paper + detailed explanation on CV method

Main concerns:

  1. Need more ablation study and discussion about PlotNet
  2. Are there any other methods for OD over plots?
  3. Limited contribution, novelty is incremental

 

ECCV2020:

Changes: CVPR paper + AN-ROI layer (info about neighbouring ROIs) + Custom Loss + Extensive ablation study with IoU based regression losses

Main concerns:

  1. Contribution & Ablation study of: (i) CV method; (ii) custom loss, (iii) linking module; is missing.
  2. description of CV based method was very vague
  3. Why are we evaluating existing OD models on plots?

 

Our Current State:

  1. we have ablation study of linking module
  2. we have comparison with latest IoU based loss functions (CIoU, DIoU, GIoU)
  3. we can write more about the thinking process which led us to our CV-based method
  4. Custom loss does not give significant improvement (our main concern)
PlotNet FrRCNN+FPN+RA MaskRCNN RetinaNet Yolo-V3
L1 loss
-log IoU
1 - IoU
Custom Loss
GIoU
L1 loss - log IoU
L1 loss + 1 - IoU
L1 loss + Custom loss
L1 loss + GIoU
  • Old linking module (v2) + loss
  • understand mAP properly
  • try different loss functions (log IOU, etc.)
  • mAP with an average pool
  • visualize why our ground-truth mAP@0.9 is not 100%
  • are beta1 and beta2 such that both the losses are in the same range
  • Custom: -(1+IOU)**gamma * log(IOU)
  • All Experiments
mAP@0.9 mAP@0.75 mAP@0.5

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
90.95 43.43 99.12 - 98.00 0.24 99.87 98.44 99.68 99.36
94.31 60.18 99.82 - 99.49 0.19 99.99 99.61 99.67 99.88
81.01% 95.34% 98.11%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%

GIOU

Smooth L1

Log IOU

83.68% 97.16% 98.20%
94.13 57.97 99.80 - 99.39 0.19 99.99 99.60 99.67 99.88

1-IOU

83.40% 97.05% 98.20%
77.22% 94.58% 97.76%
91.77% 44.68% 99.44% - 98.87% 0.24% 99.83% 98.63% 99.66% 99.65%

Custom Loss

gamma=2

81.42% 95.69% 98.14%
92.18 47.45 99.39 - 98.71 0.21 99.90 99.26 99.74 99.69

SL1 + - log IOU

81.84% 95.95% 98.27%
91.31 47.05 99.38 - 98.96 0.21 99.92 99.17 99.77 99.72

SL1 + GIOU

81.72% 96.02% 98.23%
91.47 48.64 99.44 - 98.86 0.21 99.90 99.22 99.92 99.68

SL1 + 1-IOU

81.93% 96.07% 98.24%
91.23 48.90 99.57 - 98.95 0.24 99.94 99.29 99.67 99.72

SL1 + custom loss

81.95% 95.94% 98.20%
mAP
97.76%
94.58%
77.22%
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

FrRCNN_FPN_RA

95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
0.5
0.75
0.9
0.5
0.75
0.9
98.11%
97.21%
83.89%
95.33% 91.60% 98.96% - 99.27% 99.76% 99.02% 99.77% 99.69% 99.57%
91.02% 31.69% 97.08% - 81.57% 99.36% 96.06% 85.33% 82.00% 90.95%
95.04% 86.46% 98.64% - 99.08% 99.73% 97.30% 99.59% 99.63% 99.39%

PlotNet v2

Analysis of different object detection models (PlotQA)

PlotNet v0

95.52% 91.24% 99.66% - 99.52% 99.87% 98.27% 99.83% 99.62% 99.77%
95.52% 91.24% 99.45% - 99.52% 99.56% 97.44% 99.83% 99.62% 99.77%
95.52% 91.24% 99.79% - 99.52% 99.97% 99.68% 99.83% 99.62% 99.78%
98.33%
98.14%
97.99%
0.5
0.75
0.9

#FLOPs: 283.85G

#Param: 43.81M

#FLOPs: 100.927G

#Param: 131.944M

Only

Smooth L1

PlotNet v12

95.24% 88.94% 99.01% - 99.42% 99.83% 98.06% 99.79% 99.75% 99.64%
92.16% 61.18% 98.38% - 93.46% 99.44% 97.21% 94.21% 95.45% 96.02%
95.40% 91.06% 99.29% - 99.51% 99.96% 99.56% 99.83% 99.75% 99.77%
98.24%
97.74%
91.94%
0.5
0.75
0.9

CVPR

Paper

PlotNet v30

95.27% 85.59% 99.23% - 99.48% 99.89% 97.91% 99.77% 99.80% 99.31%
92.07% 56.89% 92.50% - 93.94% 99.55% 73.97% 88.80% 83.75% 58.36%
95.43% 91.09% 99.54% - 99.50% 99.98% 99.59% 99.82% 99.83% 99.78%
98.28%
97.36%
82.20%
0.5
0.75
0.9

PlotNet v31

95.24% 89.67% 99.11% - 99.44% 99.25% 98.09% 99.77% 99.87% 99.63%
92.93% 67.94% 98.24% - 95.29% 98.38% 97.16% 94.55% 97.82% 96.06%
95.40% 90.98% 99.33% - 99.49% 99.57% 99.58% 99.81% 99.87% 99.78%
98.20%
97.79%
93.15%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 0.85)

L1 + 0.6*Custom Loss (gamma = 0.85)

PlotNet v32

95.18% 84.77% 99.32% - 99.44% 99.84% 98.07% 99.76% 99.68% 99.68%
91.88% 61.44% 96.44% - 95.58% 99.52% 97.19% 90.64% 97.55% 87.66%
95.45% 91.51% 99.54% - 99.45% 99.95% 99.63% 99.82% 99.68% 99.78%
98.31%
97.30%
90.88%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 2.0)

PlotNet v36

L1 + 0.6*Custom Loss (gamma = 2.0)

95.19% 86.04% 98.32% - 99.49% 97.42% 94.41% 99.77% 99.60% 99.67%
92.68% 61.05% 97.02% - 96.05% 94.93% 93.62% 93.29% 97.13% 95.37%
95.37% 91.22% 98.69% - 99.52% 98.35% 95.90% 99.81% 99.65% 99.76%
97.59%
96.66%
91.24%
0.5
0.75
0.9

Analysis of different object detection models (PlotQA)

Only

Smooth L1

PlotNet v12

95.24% 88.94% 99.01% - 99.42% 99.83% 98.06% 99.79% 99.75% 99.64%
92.16% 61.18% 98.38% - 93.46% 99.44% 97.21% 94.21% 95.45% 96.02%
95.40% 91.06% 99.29% - 99.51% 99.96% 99.56% 99.83% 99.75% 99.77%
98.24%
97.74%
91.94%
0.5
0.75
0.9
mAP
97.76%
94.58%
77.22%
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

FrRCNN_FPN_RA

95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
0.5
0.75
0.9

Only

log IOU

PlotNet v28

95.24% 82.69% 99.08% - 99.46% 99.85% 97.94% 99.77% 99.90% 98.82%
91.83% 45.78% 91.48% - 94.15% 98.95% 74.24% 87.19% 89.34% 50.11%
95.41% 91.02% 99.42% - 99.49% 99.97% 99.56% 99.82% 99.91% 99.77%
98.26%
96.97%
80.34%
0.5
0.75
0.9

Only

1 - IOU

PlotNet v29

95.22% 78.38% 99.02% - 99.44% 99.71% 98.10% 99.71% 99.64% 98.21%
91.79% 41.86% 93.74% - 94.64% 98.29% 83.11% 85.69% 89.32% 49.36%
95.41% 90.61% 99.35% - 99.49% 99.95% 99.67% 99.80% 99.71% 99.78%
98.20%
96.38%
80.87%
0.5
0.75
0.9

PlotNet v30

95.27% 85.59% 99.23% - 99.48% 99.89% 97.91% 99.77% 99.80% 99.31%
92.07% 56.89% 92.50% - 93.94% 99.55% 73.97% 88.80% 83.75% 58.36%
95.43% 91.09% 99.54% - 99.50% 99.98% 99.59% 99.82% 99.83% 99.78%
98.28%
97.36%
82.20%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 0.85)

PlotNet v32

95.18% 84.77% 99.32% - 99.44% 99.84% 98.07% 99.76% 99.68% 99.68%
91.88% 61.44% 96.44% - 95.58% 99.52% 97.19% 90.64% 97.55% 87.66%
95.45% 91.51% 99.54% - 99.45% 99.95% 99.63% 99.82% 99.68% 99.78%
98.31%
97.30%
90.88%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 2.0)

PlotNet v50

0.5
0.75
0.9

Only GIOU

Analysis of different object detection models (PlotQA)

mAP
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

PlotNet v24

95.18% 85.48% 98.83% - 99.26% 99.76% 97.64% 99.59% 99.62% 97.92%
90.69% 45.15% 96.30% - 86.56% 99.17% 95.72% 83.11% 85.37% 51.12%
95.32% 91.38% 98.21% - 99.41% 99.88% 99.50% 99.81% 99.89% 99.75%
98.13%
97.03%
81.46%
0.5
0.75
0.9

PlotNet v25

95.23% 86.66% 98.77% - 98.37% 99.17% 97.62% 99.50% 99.71% 97.25%
90.89% 45.56% 91.78% - 86.33% 99.40% 89.91% 79.53% 88.12% 50.27%
95.25% 91.23% 99.13% - 98.80% 99.87% 99.44% 99.80% 99.91% 99.44%
98.10%
96.92%
80.20%
0.5
0.75
0.9

PlotNet v26

95.22% 87.02% 98.95% - 99.30% 99.78% 97.88% 99.63% 99.91% 97.94%
91.83% 45.95% 94.36% - 89.83% 99.27% 95.99% 83.71% 87.84% 51.24%
95.38% 91.61% 99.21% - 99.73% 99.91% 99.55% 99.87% 99.93% 99.78%
98.33%
97.30%
82.18%
0.5
0.75
0.9

PlotNet v40

95.14% 86.69% 98.87% - 97.75% 99.83% 97.76% 99.69% 99.26% 99.26%
91.65% 48.10% 96.51% - 86.53% 99.39% 96.29% 86.78% 87.19% 72.21%
95.42% 90.73% 99.14% - 97.83% 99.96% 99.24% 99.75% 99.35% 99.76%
97.91%
97.14%
84.96%
0.5
0.75
0.9

log IoU

without AN-ROI layer

1 - IoU

Custom Loss (gamma = 0.85)

SL1 + log IoU

SL1 + 1 - IoU

PlotNet v41

95.07% 86.32% 98.32% - 97.16% 99.80% 97.77% 99.69% 99.62% 98.48%
91.32% 51.92% 96.77% - 85.84% 99.28% 96.43% 89.75% 78.33% 71.63%
95.39% 90.39% 98.74% - 98.24% 99.92% 99.42% 99.77% 99.62% 98.91%
97.82%
96.92%
84.59%
0.5
0.75
0.9

SL1 + Custom

PlotNet v39

95.17% 87.37% 98.91% - 97.92% 99.86% 97.78% 99.73% 99.81% 99.31%
91.94% 51.98% 97.49% - 90.01% 99.59% 96.68% 89.78% 87.25% 73.92%
95.54% 91.11% 99.26% - 98.25% 99.97% 99.46% 99.79% 99.83% 99.91%
98.12%
97.32%
86.51%
0.5
0.75
0.9

Analysis of different object detection models (PlotQA)

mAP
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

PlotNet v30

95.27% 85.59% 99.23% - 99.48% 99.89% 97.91% 99.77% 99.80% 99.31%
92.07% 56.89% 92.50% - 93.94% 99.55% 73.97% 88.80% 83.75% 58.36%
95.43% 91.09% 99.54% - 99.50% 99.98% 99.59% 99.82% 99.83% 99.78%
98.28%
97.36%
82.20%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 0.85)

PlotNet v32

95.18% 84.77% 99.32% - 99.44% 99.84% 98.07% 99.76% 99.68% 99.68%
91.88% 61.44% 96.44% - 95.58% 99.52% 97.19% 90.64% 97.55% 87.66%
95.45% 91.51% 99.54% - 99.45% 99.95% 99.63% 99.82% 99.68% 99.78%
98.31%
97.30%
90.88%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 2.0)

PlotNet v34

95.22% 85.95% 99.15% - 99.39% 99.82% 97.59% 99.75% 99.78% 99.36%
92.27% 65.13% 93.99% - 95.82% 99.26% 91.31% 89.00% 95.39% 65.82%
95.42% 91.37% 99.42% - 99.42% 99.93% 99.50% 99.81% 99.79% 99.76%
98.27%
97.33%
87.55%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 3.0)

PlotNet v35

95.29% 83.75% 99.07% - 88.56% 99.72% 98.03% 99.66% 99.73% 99.68%
92.50% 59.76% 97.03% - 86.39% 99.31% 96.74% 89.70% 96.94% 92.89%
95.47% 91.59% 99.25% - 88.60% 99.82% 99.62% 99.70% 99.79% 99.77%
97.07%
95.94%
90.14%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 4.0)

PlotNet v44

94.98% 85.00% 99.26% - 99.47% 99.55% 98.01% 99.73% 99.94% 99.45%
92.03% 60.37% 95.23% - 95.11% 98.85% 97.02% 88.88% 96.50% 81.45%
95.14% 90.11% 99.50% - 99.51% 99.82% 99.58% 99.82% 99.95% 99.77%
98.13%
97.27%
89.49%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 1.0)

PlotNet v45

95.14% 79.49% 99.08% - 99.50% 99.76% 97.65% 99.73% 99.60% 98.59%
92.14% 55.59% 93.69% - 95.57% 99.43% 90.24% 84.96% 91.33% 58.70%
95.31% 91.25% 99.33% - 99.51% 99.88% 99.54% 99.81% 99.67% 99.72%
98.22%
96.50%
84.63%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 1.25)

PlotNet v46

95.27% 78.47% 98.08% - 99.35% 96.95% 96.31% 99.75% 99.56% 99.02%
92.06% 51.74% 94.84% - 94.98% 94.84% 93.31% 89.53% 93.79% 55.31%
95.43% 91.50% 99.22% - 99.35% 97.92% 97.95% 99.81% 99.58% 99.73%
97.83%
95.94%
84.49%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 1.5)

PlotNet v47

95.01% 82.51% 98.96% - 99.46% 99.85% 98.00% 99.76% 99.66% 99.59%
91.99% 56.92% 95.70% - 95.67% 99.49% 96.43% 92.12% 95.34% 87.63%
95.22% 91.12% 99.41% - 99.51% 99.95% 99.53% 99.82% 99.67% 99.74%
98.22%
96.98%
90.14%
0.5
0.75
0.9

Only Custom Loss (gamma = 0.85)

Only Custom Loss (gamma = 1.75)

Analysis of different object detection models (PlotQA)

mAP
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

PlotNet v42

95.24% 85.56% 99.04% - 99.36% 95.66% 97.03% 99.76% 99.90% 99.42%
92.94% 59.12% 94.87% - 95.58% 92.46% 94.46% 94.29% 95.36% 76.85%
95.37% 91.08% 99.28% - 99.53% 96.88% 98.77% 99.79% 99.90% 99.75%
97.82%
96.78%
88.44%
0.5
0.75
0.9

SL1 + log IOU

PlotNet v43

95.21% 89.84% 99.42% - 99.51% 99.84% 98.13% 99.80% 99.92% 99.69%
92.80% 70.11% 98.47% - 96.33% 99.27% 97.31% 94.12% 97.66% 94.42%
95.38% 91.19% 99.60% - 99.52% 99.97% 99.66% 99.82% 99.93% 99.77%
98.32%
97.93%
93.39%
0.5
0.75
0.9

PlotNet v48

SL1 + Custom Loss (gamma = 2.0)

SL1 + 1- IOU

95.23% 89.30% 99.35% - 99.49% 99.80% 96.80% 99.79% 99.86% 99.63%
92.78% 68.26% 97.75% - 95.90% 99.04% 93.64% 92.97% 96.24% 93.12%
95.44% 91.25% 99.60% - 99.53% 99.97% 98.39% 99.82% 99.88% 99.77%
98.18%
97.70%
92.19%
0.5
0.75
0.9

PlotNet v51

0.5
0.75
0.9

SL1 + Only GIOU

mAP
97.76%
94.58%
77.22%
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

FrRCNN_FPN_RA

95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
0.5
0.75
0.9
0.5
0.75
0.9
98.11%
97.21%
83.89%
95.33% 91.60% 98.96% - 99.27% 99.76% 99.02% 99.77% 99.69% 99.57%
91.02% 31.69% 97.08% - 81.57% 99.36% 96.06% 85.33% 82.00% 90.95%
95.04% 86.46% 98.64% - 99.08% 99.73% 97.30% 99.59% 99.63% 99.39%

PlotNet v2

Analysis of different object detection models (PlotQA)

PlotNet v3

95.06% 88.81% 99.14% - 99.14% 99.79% 98.08% 99.75% 99.65% 99.65%
91.35% 52.85% 98.06% - 88.65% 99.24% 97.00% 90.32% 93.36% 94.55%
95.35% 90.97% 99.42% - 99.46% 99.91% 99.61% 99.81% 99.72% 99.77%
98.22%
97.67%
89.49%
0.5
0.75
0.9
0.5
0.75
0.9
98.19%
97.63%
90.93%
95.39% 91.39% 99.19% - 99.53% 99.65% 99.14% 99.82% 99.81% 99.77%
91.61% 55.72% 98.21% - 91.46% 97.33% 96.75% 94.36% 97.24% 95.67%
95.10% 89.25% 99.01% - 99.41% 99.14% 97.52% 99.77% 99.81% 99.70%

PlotNet v4

PlotNet v0

95.52% 91.24% 99.66% - 99.52% 99.87% 98.27% 99.83% 99.62% 99.77%
95.52% 91.24% 99.45% - 99.52% 99.56% 97.44% 99.83% 99.62% 99.77%
95.52% 91.24% 99.79% - 99.52% 99.97% 99.68% 99.83% 99.62% 99.78%
98.33%
98.14%
97.99%
0.5
0.75
0.9

#FLOPs: 283.85G

#Param: 43.81M

#FLOPs: 100.927G

#Param: 131.944M

#FLOPs: 100.927G

#Param: 131.944M

#FLOPs: 100.927G

#Param: 131.944M

PlotNet v12

95.24% 88.94% 99.01% - 99.42% 99.83% 98.06% 99.79% 99.75% 99.64%
92.16% 61.18% 98.38% - 93.46% 99.44% 97.21% 94.21% 95.45% 96.02%
95.40% 91.06% 99.29% - 99.51% 99.96% 99.56% 99.83% 99.75% 99.77%
98.24%
97.74%
91.94%
0.5
0.75
0.9

PlotNet v14

95.15% 88.71% 98.37% - 99.09% 99.79% 97.75% 99.73% 99.75% 99.43%
90.05% 40.38% 96.55% - 88.16% 99.19% 96.42% 89.49% 86.98% 89.03%
95.36% 91.12% 98.77% - 99.14% 99.93% 99.40% 99.80% 99.77% 99.68%
98.11%
97.53%
86.25%
0.5
0.75
0.9
0.5
0.75
0.9
97.90%
97.12%
85.68%
95.41% 89.75% 98.99% - 99.12% 99.86% 98.64% 99.78% 99.86% 99.73%
91.47% 35.11% 97.37% - 85.78% 99.22% 96.01% 88.77% 87.35% 90.02%
95.21% 85.61% 98.69% - 98.84% 99.74% 96.98% 99.71% 99.79% 99.53%

PlotNet v15

mAP
97.76%
94.58%
77.22%
IOU
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel

FrRCNN_FPN_RA

95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
0.5
0.75
0.9
0.5
0.75
0.9
98.16%
97.49%
90.17%
95.42% 91.12% 98.98% - 99.34% 99.93% 99.13% 99.82% 99.91% 99.76%
90.87% 52.84% 97.68% - 90.27% 99.37% 96.55% 92.97% 96.34% 94.67%
95.11% 87.89% 98.59% - 99.17% 99.80% 97.55% 99.77% 99.91% 99.63%

PlotNet v8

Analysis of different object detection models (PlotQA)

PlotNet v7

95.17% 87.85% 99.05% - 99.29% 99.81% 97.87% 99.75% 99.96% 99.64%
91.62% 48.07% 98.08% - 89.12% 99.29% 96.97% 93.00% 96.63% 94.72%
95.40% 90.91% 99.31% - 99.48% 99.93% 99.53% 99.81% 99.96% 99.77%
98.23%
97.60%
89.72%
0.5
0.75
0.9

PlotNet v1

94.97% 87.13% 97.42% - 97.87% 99.11% 96.38% 99.68% 99.02% 99.04%
89.46% 37.63% 93.64% - 78.12% 95.22% 94.06% 88.10% 66.95% 83.86%
95.32% 90.65% 97.83% - 98.31% 99.66% 98.19% 99.78% 99.02% 99.40%
97.57%
96.74%
80.78%
0.5
0.75
0.9
0.5
0.75
0.9
97.62%
94.03%
78.71%
95.32% 86.32% 99.37% - 99.46% 99.95% 98.93% 99.80% 99.70% 99.76%
87.96% 6.30% 95.11% - 78.23% 99.28% 94.62% 80.52% 84.85% 81.51%
94.79% 58.12% 99.06% - 98.57% 99.83% 97.16% 99.64% 99.64% 99.43%

PlotNet v5

  1. Table F1

  2. Template wise answer distribution Table

  3. Human Accuracy

  4. Comparison Table (FQA, DVQA, PlotQA) like Table 1 in Kushal's paper

  5. Number of questions with a particular question length (X=Q-length, Y=Number of Qs)->histogram

  6. Average Q-length, Min Q-length and Max Q-length

  7. Number of unique Qs compare with other datasets

  8. Number of Qs answered by varying vocab. size

IOU@0.75 IOU@0.9 IOU@0.5
mAP
Table F1-score
Structural Data Retrieval Reasoning
Yes/No
Fixed vocab.
Open Vocab.

PlotQA Answer Distribution

Model Binary Fixed Vocab. OOV
SAN (6.08%) 80.20% 19.80% 0.00%
VOES (18.46%) 0.00% 13.43% 20.60%

PlotQA data distribution

Dataset Split Images QA-pairs (old) QA-pairs (new)
Train 157070 5,733,893 20,249,479
Validation 33650 1,228,468 4,360,648
Test 33657 1,228,313 4,342,514
Total 224,377 8,190,674 28,952,641

ANSWERS

Old Distribution

New Distribution

PlotQA data distribution on TEST split

Yes/No Fixed Vocab OOV
23.46% 76.53% 0.00%
Yes/No Fixed Vocab OOV
27.46% 46.1% 26.4%
Datasets DVQA
Methods\Templates
Distribution
PlotQA
Yes/No Fixed Vocab OOV
3.86 15.38 80.76
Datasets PlotQA (Top-1k)
Methods\Templates
Distribution
PlotQA (Top-5k) PlotQA (Top-10k)
Yes/No Fixed Vocab OOV
3.86 26.90 69.24
Yes/No Fixed Vocab OOV
3.86 33.65 62.49

TEMPLATES

Structure Data Reasoning
13.48% 31.93% 54.59%
Structure Data Reasoning
30.37% 23.97% 45.66%
Datasets DVQA
Methods\Templates
Distribution
PlotQA

Old Distribution

New Distribution

Structure Data Reasoning
13.48% 31.93% 54.59%
Structure Data Reasoning
4.30% 13.73% 81.97%
Datasets DVQA
Methods\Templates
Distribution
PlotQA

PlotQA data distribution on TEST split

Structure Data Reasoning
82.13% 15.02% 14%
17.8% 84.98% 85.91%
0.00% 0.00% 0.00%
Yes/No
Fixed Vocab
OOV
Structure Data Reasoning
37.59% 20.85% 24.18%
62.4% 56.3% 29.89%
0.00% 22.84% 45.92%
Datasets DVQA DIP
Answer \ Template
Structure Data Reasoning
82.13% 15.02% 14%
17.8% 84.98% 85.91%
0.00% 0.00% 0.00%
Yes/No
Fixed Vocab
OOV
Structure Data Reasoning
37.64% 5.14% 1.88%
62.36% 16.01% 12.80%
0.00% 78.85% 85.32%
Datasets DVQA DIP
Answer \ Template

Old Distribution

New Distribution

PlotQA data distribution on TEST split

TEMPLATES & ANSWERS

*keeping top-1K answers in fixed vocab

Accuracy of different models (in %)

Structure Data Reasoning
94.01 95.35 66.02
77.30 32.06 29.27
NA 0.90 3.40
0.00 0.00 0.00
42.29 27.61 25.48
NA 32.00 15.44
94.01 95.35 66.02
81.66 40.60 35.74
NA 32.06 17.10
Yes/No
Fixed Vocab
OOV
Yes/No
Fixed Vocab
OOV
Yes/No
Fixed Vocab
OOV
SAN
 
VOES
 
MaskRCNN FrRCNN-FPN-RA
Answer \ Template
SAN-
VOES

Zooming in the accuracy of different models

* All accuracies are calculated with 5% threshold

Structure Data Reasoning
94.01 95.35 66.02
77.30 32.06 29.27
NA 0.90 3.40
0.00 0.00 0.00
42.72 29.71 31.19
NA 40.66 26.05
94.01 95.35 66.02
77.09 39.84 38.75
NA 40.66 26.05
Model MaskRCNN FrRCNN-FPN-RA
SAN 46.54 46.54
VOES 20.22 24.08
SAN-VOES 53.96 55.75

Overall Model Accuracy

Accuracy of different models (in %)

Structure Data Reasoning
91.12 97.32 62.75
66.85 30.76 16.03
NA 0.00 0.00
0.00 0.00 0.00
42.12 16.07 7.24
NA 57.39 14.95
91.12 97.32 62.75
66.86 22.64 7.95
NA 57.39 14.95
Yes/No
Fixed Vocab
OOV
Yes/No
Fixed Vocab
OOV
Yes/No
Fixed Vocab
OOV
SAN
 
VOES
 
~FrRCNN-FPN-RA (new dataset) FrRCNN-FPN-RA (old dataset)
Answer \ Template
SAN-
VOES

Zooming in the accuracy of different models

* All accuracies are calculated with 5% threshold

Structure Data Reasoning
94.01 95.35 66.02
77.30 32.06 29.27
NA 0.90 3.40
0.00 0.00 0.00
42.72 29.71 31.19
NA 40.66 26.05
94.01 95.35 66.02
77.09 39.84 38.75
NA 40.66 26.05
Model FrRCNN-FPN-RA
(new dataset)		 FrRCNN-FPN-RA
(old dataset)
SAN 7.76 46.54
VOES* 18.46 (approx) 24.08
SAN-VOES 22.52 55.75

Overall Model Accuracy (in %)

Look, Read, Reason and Answer

https://ppasupat.github.io/resource/THESIS-v3e.pdf

Q: Where did the last 1st place finish occur?

A: Thailand

To see the numbers, click here

IOU v/s mAP for different object detection models

FRCNN_SS

FrRCNN_RP

SSD

RetinaNet

FRCNN_FPN_SS

FrRCNN_RA

FrRCNN_FPN_RA

YoloV3

To see the numbers, click here

mAP v/s Table F1 score for different object detection models

SS RPN Grid ROI-Pool ROI-Align CNN FPN OHEM FL mAP
R-50 19.47%
R-50 58.58%
R-50 3.81%
R-50 51.42%
R-50 77.22%
R-50 33.86%
N.A N.A Darknet 7.56%
N.A N.A R-50 30.07%
N.A N.A I-Net 1.45%
FRCNN
FRCNN_FPN
FrRCNN_RP
FrRCNN_RA
FrRCNN_FPN_RA
Mask-RCNN
YOLO-V3
RetinaNet
SSD
Proposal Method Pooling Method Feature Extraction Class Imbalance
CV ROI-Align CNN FPN Linking AvgPool mAP
R-10 80.68%
R-10 A0 68.18%
R-10 A1 72.87%
R-10 A2 79.80%
R-10 A3 80.42%
R-22 83.89%
R-22 A0 71.14%
R-22 A1 73.23%
R-22 A2 74.51%
R-22 A3 81.61%
R-50 59.28%
R-50 74.08%
PlotQA_R10
PlotQA_R10
PlotQA_R10
PlotQA_R10
PlotQA_R10
PlotQA_R22_FPN
PlotQA_R22_FPN
PlotQA_R22_FPN
PlotQA_R22_FPN
PlotQA_R22_FPN
PlotQA_R50
PlotQA_R50_FPN

*We are not doing anything for class-Imbalance

IoU@0.5 IoU@0.75 IoU@0.9
FRCNN
FRCNN_FPN
FrRCNN_RP
FrRCNN_RA
FrRCNN_FPN_RA
Mask-RCNN
YOLO-V3
RetinaNet
SSD

Table Accuracy

Experiments

R50

R-

clipped

with FPN

without FPN

AvgPool

~AvgPool

~AvgPool

mAP

#FLOPs

#Params

Time

Table-F1

mAP

#FLOPs

#Params

Time

Table-F1

74.08%

119.38G

56.72M

59.28%

113.02G

233.2M

83.89%

96.14G

131.82M

80.68%

35.09G

130.44M

0.026s

0.032s

-

-

-

-

-

-

-

-

-

-

0.055s

-

-

0.040s

AvgPool

17.65G

42.36M

0.040s

61.28G

43.74M

0.055s

80.42%

51.42%

1.29T

35.9M

77.22%

283.85G

43.81M

0.374s

Fr-

RCNN

AvgPool

(i) mAP is reported at 0.9 IOU

(ii) Table F1-score is calculated using 5% relaxation for numeric values

Selective Search RPN+FPN CV CV+FPN
#Proposals ~2000 ~225120 ~90 ~90*#Levels

82.92%

0.57

0.70

0.72

Table Accuracy at 5% relaxation for numeric values

R-50_FPN

R-22_FPN

R-10

FrRCNN_FPN_RA

R-50

IOU Precision Recall F1-score
0.5
0.75
0.9
IOU Precision Recall F1-score
0.5
0.75
0.9

PlotQA

Experiments

R50

R-

clipped

with FPN

without FPN

AvgPool

AvgPool-A0

~AvgPool

~AvgPool

mAP

#FLOPs

#Params

Time

Table-F1

mAP

#FLOPs

#Params

Time

Table-F1

74.08%

119.38G

56.72M

59.28%

113.02G

233.2M

83.89%

96.14G

131.82M

80.68%

35.09G

130.44M

0.026s

0.032s

54.62G

26.943M

0.024s

-

-

-

-

-

-

-

-

-

-

0.025s

71.14%

-

-

0.016s

14.33G

25.57M

0.016s

68.18%

AvgPool-A1

AvgPool-A2

AvgPool-A3

16.19G

35.02M

0.016s

17.65G

42.36M

0.016s

14.85G

28.21M

0.016s

55.66G

29.583M

0.025s

58.37G

36.40M

0.025s

61.28G

43.74M

0.025s

73.23%

74.51%

72.87%

79.80%

80.42%

51.42%

1.29T

35.9M

77.22%

283.85G

43.81M

0.374s

Fr-

RCNN

AvgPool

(i) mAP is reported at 0.9 IOU

(ii) Table F1-score is calculated using 5% relaxation for numeric values

Selective Search RPN+FPN CV CV+FPN
#Proposals ~2000 ~225120 ~90 ~90*#Levels

82.92%

0.57

0.70

0.72

Analysis of different object detection models (PlotQA)

R-50_FPN

R-22_FPN

95.04% 86.46% 98.64% - 99.08% 99.73% 97.30% 99.59% 99.63% 99.39%
91.02% 31.69% 97.08% - 81.57% 99.36% 96.06% 85.33% 82.00% 90.95%
95.33% 91.60% 98.96% - 99.27% 99.76% 99.02% 99.77% 99.69% 99.57%
98.11%
97.21%
83.89%
0.5
0.75
0.9

R-10

94.97% 87.13% 97.42% - 97.87% 99.11% 96.38% 99.68% 99.02% 99.04%
89.46% 37.63% 93.64% - 78.12% 95.22% 94.06% 88.10% 66.95% 83.86%
95.32% 90.65% 97.83% - 98.31% 99.66% 98.19% 99.78% 99.02% 99.40%
97.57%
96.74%
80.78%
0.5
0.75
0.9
94.98% 63.22% 97.5% - 98.17% 99.61% 95.92% 99.46% 99.76% 98.22%
90.77% 5.12% 95.58% - 80.72% 99.16% 94.79% 76.83% 65.56% 58.17%
95.28% 90.55% 98.11% - 98.64% 99.69% 97.49% 99.70% 99.82% 99.77%
97.67%
94.09%
74.08%
0.5
0.75
0.9
0.5
0.75
0.9
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
IOU
97.76%
94.58%
77.22%
mAP

R-50

93.66% 71.68% 97.85% - 94.72% 99.80% 97.46% 98.58% 97.46% 93.97%
87.64% 15.72% 74.57% - 41.87% 98.92% 81.60% 54.21% 43.35% 35.67%
93.97% 89.42% 98.80% - 98.84% 99.85% 99.38% 99.47% 99.54% 99.76%
97.67%
93.91%
59.28%
0.5
0.75
0.9

R-22_FPN

(avgpool)

0.5
0.75
0.9

R-10

(avgpool)

0.5
0.75
0.9
95.14% 85.08% 98.81% - 97.46% 99.71% 97.22% 99.66% 99.33% 99.43%
91.13% 32.03% 97.26% - 85.15% 99.32% 95.43% 87.42% 83.50% 75.02%
95.37% 91.18% 99.09% - 97.67% 99.80% 98.96% 99.73% 99.41% 99.70%
97.88%
96.87%
82.92%
94.89% 87.25% 97.56% - 98.79% 99.58% 92.86% 99.65% 99.75% 99.29%
89.61% 36.31% 92.40% - 82.23% 98.18% 88.27% 85.66% 71.14% 79.96%
95.23% 91.22% 98.04% - 99.34% 99.73% 95.39% 99.76% 99.78% 99.70%
97.58%
96.63%
80.42%
91.59% 77.72% 89.15% 35.24% 67.96% 7.89% 77.59% 92.93% 55.06% 89.74%
87.59% 42.77% 79.05% 20.46% 66.39% 0.22% 69.78% 88.29% 46.63% 84.60%
92.05% 79.30% 89.55% 45.42% 68.00% 18.51% 80.07% 93.07% 56.32% 89.76%
71.21%
68.49%
58.58%

FRCNN_

FPN_SS

91.23% 78.60% 97.87% 0.00% 97.46% 29.13% 87.96% 98.37% 84.87% 99.13%
55.73% 0.73% 34.91% 0.00% 62.27% 0.87% 49.02% 63.99% 23.60% 71.44%
92.45% 93.56% 99.92% 0.00% 98.05% 88.88% 98.76% 99.67% 99.15% 99.71%
87.02%
76.46%
36.26%
0.5
0.75
0.9

FRCNN_

CV

81.99% 79.55% 99.34% 30.93% 95.70% 49.36% 97.75% 95.59% 99.42% 94.85%
47.54% 4.96% 50.83% 5.83% 32.43% 0.33% 46.2% 33.72% 80.53% 36.31%
91.24% 95.05% 99.77% 51.83% 99.87% 99.91% 99.94% 99.75% 99.97% 99.84%
93.72%
82.45%
33.86%
0.5
0.75
0.9

Mask-RCNN

IoU
0.5
0.75
0.9
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
76.92% 71.67% 92.13% 50.17% 94.44% 17.10% 90.59% 84.17% 80.90% 62.68%
72.08%
15.51% 10.90% 7.15% 6.54% 11.70% 0.02% 4.39% 8.08% 9.59% 1.70%
7.56%
87.95% 94.13% 99.95% 66.25% 99.94% 99.58% 99.99% 99.89% 99.77% 99.64%
94.71%

YOLO-V3

0.5
0.75
0.9
24.92% 18.21% 44.27% 22.71% 30.29% 45.05% 75.62% 34.81% 66.07% 16.93%
37.89%
1.39% 0.04% 2.18% 1.25% 0.39% 0.04% 3.39% 0.44% 5.14% 0.20%
1.45%
61.09% 74.27% 71.15% 47.41% 78.61% 99.80% 99.95% 93.46% 97.35% 78.72%
80.18%

SSD

0.5
0.75
0.9
24.37% 0.00% 88.62% 22.49% 99.03% 0.00% 66.20% 66.77% 37.60% 42.83%
14.67% 0.00% 65.33% 7.43% 64.38% 0.00% 57.42% 99.73% 31.77% 29.97%
27.76% 0.00% 91.43% 28.62% 99.42% 0.00% 66.77% 67.41% 38.00% 43.05%
46.24%
44.79%
30.07%
0.5
0.75
0.9

Retina-Net

mAP

Analysis of different object detection models

Our Model

0.5
0.75
0.9

FrRCNN_FPN_RA

0.5
0.75
0.9
94.30% 78.59% 99.96% - 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
85.54% 27.86% 93.68% - 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
95.84% 84.28% 99.99% - 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
97.76%
94.58%
77.22%

Analysis of different object detection models (DVQA)

0.5
0.75
0.9
95.80% - 99.92% - 99.89% 99.99% 100% 99.66% 100% 99.72%
82.10% - 96.52% - 98.17% 99.24% 98.79% 96.00% 98.51% 98.33%
98.28% - 99.99% - 99.95% 99.99% 100% 99.83% 100% 99.82%

FrRCNN_FPN_RA

bar dotline legend-label line heading title xlabel xticklabel ylabel yticklabel
IOU
99.73%
99.37%
95.96%
mAP

Analysis of different object detection models

Model ID Backbone CNN Proposal Method Additional information Object Detection Model
FRCNN_CV ResNet50 CV method 2-stage detector, RoiAlign Fast RCNN
FRCNN_SS ResNet50 Selective Search 2-stage detector, RoiAlign Fast RCNN
FRCNN_FPN_SS ResNet50-FPN Selective Search 2-stage detector Fast RCNN
FrRCNN_RA ResNet50 RPN 2-stage detector, RoiAlign Faster RCNN
FrRCNN_FPN_RA ResNet50-FPN RPN 2-stage detector, RoiAlign Faster RCNN
FrRCNN_RP ResNet50 RPN 2-stage detector, RoiPool Faster RCNN
RetinaNet ResNet50-FPN fixed anchor 1-stage detector RetinaNet
MaskRCNN ResNet50 RPN+segmentation 2-stage detector Mask RCNN
SSD InceptionNet fixed anchor 1-stage detector SSD
YoloV3 DarkNet-106 layers k-means+anchor 1-stage detector YoloV3
Backbone Segmentation Procedure Model Accuracy
SegNet
PixelLink

Model descriptions and Model_IDs

FRCNN_CV FRCNN_SS FRCNN_FPN_SS FrRCNN_RP FrRCNN_RA FrRCNN_FPN_RA Mask-RCNN SSD YoloV3 RetinaNet ( FL )
92.45% 90.71% 92.05% 86.72% 93.05% 95.84% 91.24% 61.09% 87.95% 27.76%
93.57% 82.4% 79.30% 73.28% 95.46% 96.30% 95.05% 74.27% 94.13% 0.00%
99.92% 90.23% 89.55% 97.68% 99.99% 99.99% 99.77% 71.15% 99.95% 91.43%
0.00% 37.00% 45.42% 56.64% 62.83% 72.25% 51.83% 47.41% 66.25% 28.62%
98.05% 74.78% 68.00% 92.69% 99.99% 99.95% 99.87% 78.61% 99.94% 99.42%
88.88% 17.59% 18.51% 83.15% 99.99% 100% 99.91% 99.80% 99.58% 0.00%
98.76% 78.59% 80.07% 95.99% 99.99% 99.99% 99.94% 99.95% 99.99% 66.77%
99.67% 93.22% 93.07% 93.28% 99.88% 99.92% 99.75% 93.46% 99.89% 67.41%
99.15% 59.12% 56.32% 97.86% 99.90% 99.90% 99.97% 97.35% 99.77% 38.00%
99.71% 91.54% 89.76% 84.08% 99.87% 99.99% 99.84% 78.72% 99.64% 43.05%
87.02% 71.52% 71.21% 86.14% 95.09% 96.43% 93.72% 80.18% 94.71% 46.24%

 
bar
dotline
legend-label
line
preview
title
xlabel
xticklabel
ylabel
yticklabel
mAP

Trained at

IOU@0.1

Analysis of different object detection models

Results are shown on Test split by keeping IoU 0.5

RetinaNet ( CE )
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
00.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.23% 78.60% 97.87% 0.00% 97.46% 29.13% 87.96% 98.37% 84.87% 99.13%
mAP @ 0.75
76.46%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
55.73% 0.73% 34.91% 0.00% 62.27% 0.87% 49.02% 63.99% 23.60% 71.44%
mAP @ 0.9
36.26%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
92.45% 93.56% 99.92% 0.00% 98.05% 88.88% 98.76% 99.67% 99.15% 99.71%
mAP @ 0.5
87.02%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
92.72% 94.69% 99.95% 0.03% 98.16% 91.00% 98.86% 99.71% 99.29% 99.78%
mAP @ 0.1
87.42%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%

FRCNN_CV

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
85.20% 58.28% 82.33% 21.39% 65.27% 2.21% 71.95% 90.04% 47.9% 88.38%
mAP @ 0.75
61.29%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
53.38% 0.22% 12.59% 3.13% 14.06% 0.03% 42.13% 25.49% 11.68% 31.98%
mAP @ 0.9
19.47%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
90.71% 82.40% 90.23% 37.00% 74.78% 17.59% 78.59% 93.22% 59.12% 91.54%
mAP @ 0.5
71.52%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.61% 83.75% 90.83% 55.78% 82.81% 22.55% 82.54% 93.25% 69.75% 91.61%
mAP @ 0.1
76.45%

FRCNN_SS

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.59% 77.72% 89.15% 35.24% 67.96% 7.89% 77.59% 92.93% 55.06% 89.74%
mAP @ 0.75
68.49%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
87.59% 42.77% 79.05% 20.46% 66.39% 0.22% 69.78% 88.29% 46.63% 84.60%
mAP @ 0.9
58.58%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
92.05% 79.30% 89.55% 45.42% 68.00% 18.51% 80.07% 93.07% 56.32% 89.76%
mAP @ 0.5
71.21%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
92.47% 79.33% 89.59% 59.53% 68.07% 19.25% 80.70% 93.08% 56.53% 89.78%
mAP @ 0.1
72.83%

FRCNN_FPN_SS

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
59.06% 36.74% 73.85% 32.13% 38.80% 4.51% 60.18% 59.10% 74.26% 50.41%
mAP @ 0.75
48.90%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
6.92% 1.68% 7.78% 1.10% 1.45% 0.00% 4.35% 6.10% 3.57% 5.18%
mAP @ 0.9
3.81%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
86.72% 73.28% 97.68% 56.64% 92.69% 83.15% 95.99% 93.28% 97.86% 84.08%
mAP @ 0.5
86.14%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
88.96% 76.48% 97.77% 67.77% 92.98% 88.16% 96.23% 93.81% 98.1% 84.58%
mAP @ 0.1
88.48%

FrRCNN_RP

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
86.92% 87.32% 99.77% 44.55% 99.59% 55.02% 99.90% 98.08% 99.84% 97.27%
mAP @ 0.75
86.83%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
63.89% 12.82% 70.95% 16.76% 60.61% 0.18% 83.88% 60.76% 93.47% 50.87%
mAP @ 0.9
51.42%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.05% 95.46% 99.99% 62.83% 99.99% 99.99% 99.99% 99.88% 99.90% 99.87%
mAP @ 0.5
95.09%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
95.13% 96.22% 99.99% 75.76% 99.99% 99.99% 99.99% 99.92% 99.90% 99.92%
mAP @ 0.1
96.68%

FrRCNN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
94.30% 95.14% 99.96% 62.04% 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
mAP @ 0.75
92.98%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
85.54% 18.07% 93.68% 37.65% 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
mAP @ 0.9
72.29%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
95.84% 96.30% 99.99% 72.25% 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
mAP @ 0.5
96.43%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
96.35% 96.51% 99.99% 82.72% 99.95% 100.00% 100.00% 99.94% 99.90% 99.99%
mAP @ 0.1
97.53%

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
24.37% 0.00% 88.62% 22.49% 99.03% 0.00% 66.20% 66.77% 37.60% 42.83%
mAP @ 0.75
44.79%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
14.67% 0.00% 65.33% 7.43% 64.38% 0.00% 57.42% 29.73% 31.77% 29.97%
mAP @ 0.9
30.07%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
27.76% 0.00% 91.43% 28.62% 99.42% 0.00% 66.77% 67.41% 38.00% 43.05%
mAP @ 0.5
46.24%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
32.69% 0.00% 93.24% 31.13% 99.42% 0.00% 69.45% 67.46% 38.05% 43.12%
mAP @ 0.1
47.46%

RetinaNet

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
24.92% 18.21% 44.27% 22.71% 30.29% 45.05% 75.62% 34.81% 66.07% 16.93%
mAP @ 0.75
37.89%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
1.39% 0.04% 2.18% 1.25% 0.39% 0.04% 3.39% 0.44% 5.14% 0.20%
mAP @ 0.9
1.45%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
61.09% 74.27% 71.15% 47.41% 78.61% 99.80% 99.95% 93.46% 97.35% 78.72%
mAP @ 0.5
80.18%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
84.75% 90.12% 78.79% 64.84% 86.09% 99.94% 99.99% 99.76% 99.75% 97.29%
mAP @ 0.1
90.13%

SSD

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
81.99% 79.55% 99.34% 30.93% 95.70% 49.36% 97.75% 95.59% 99.42% 94.85%
mAP @ 0.75
82.45%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
47.54% 4.96% 50.83% 5.83% 32.43% 0.33% 46.2% 33.72% 80.53% 36.31%
mAP @ 0.9
33.86%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.24% 95.05% 99.77% 51.83% 99.87% 99.91% 99.94% 99.75% 99.97% 99.84%
mAP @ 0.5
93.72%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.76% 96.32% 99.77% 65.57% 99.91% 99.94% 99.95% 99.84% 99.97% 99.91%
mAP @ 0.1
95.50%

MaskRCNN

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.00%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
76.92% 71.67% 92.13% 50.17% 94.44% 17.10% 90.59% 84.17% 80.90% 62.68%
mAP @ 0.75
72.08%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
15.51% 10.90% 7.15% 6.54% 11.70% 0.02% 4.39% 8.08% 9.59% 1.70%
mAP @ 0.9
7.56%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
87.95% 94.13% 99.95% 66.25% 99.94% 99.58% 99.99% 99.89% 99.77% 99.64%
mAP @ 0.5
94.71%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.04% 94.39% 99.96% 69.57% 99.97% 96.61% 99.99% 99.94% 99.80% 99.99%
mAP @ 0.1
95.63%

YoloV3

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
0.00% 0.92% 0.00% 0.00% 0.40% 0.00% 0.00% 0.00% 0.00% 0.00%
mAP @ 1.0
0.10%

Demystifying mAP

if IOU(pred, gt) >= threshold:

        if pred-label == gt-label:

                TP += 1

        elif pred-label != gt-label:

                FN += 1

else:

        FP += 1

TP, FP, FN

  • True Positive (TP): A correct detection. Detection with IOU ≥ threshold
  • False Positive (FP): A wrong detection. Detection with IOU < threshold
  • False Negative (FN): A ground truth not detected
  • True Negative (TN): Does not apply. It would represent a corrected misdetection. In the object detection task there are many possible bounding boxes that should not be detected within an image. Thus, TN would be all possible bounding boxes that were corrrectly not detected (so many possible boxes within an image). That's why it is not used by the metrics.

Source: https://blog.objectivity.co.uk/comparing-object-detection-models/

Demystifying mAP

There are 7 images with 15 ground-truth objects represented by the green bounding boxes and 24 detected objects represented by the red bounding boxes. Each detected object has a confidence level and is identified by a letter (A, B,..., Y).

There are 7 images with 15 ground-truth objects represented by the green bounding boxes and 24 detected objects represented by the red bounding boxes. Each detected object has a confidence level and is identified by a letter (A, B,..., Y).

Precision-Recall Curve

11-point interpolation

11-point interpolation

Interpolated Precision

Class Precision Recall
bar 0.8 0.89
title 0.04 0.05
xlabel 0.98 0.99
ylabel 0.99 1.0
xticklabel 0.97 0.97
yticklabel 0.98 0.98
legend-label 0.95 0.97
preview 0.97 0.97
dot-line 0.42 0.41
line 0.26 0.49

FrRCNN_FPN_RA

Class Precision Recall
bar 0.94 0.91
title 0.56 0.63
xlabel 0.82 0.88
ylabel 0.65 0.68
xticklabel 0.42 0.42
yticklabel 0.36 0.23
legend-label 0.87 0.84
preview 0.82 0.79
dot-line 0.38 0.24
line 0.35 0.26

PlotQA_R22_FPN

IOU@0.9

gt      = [26.0, 551.0, 52.44, 564]

pred = [27.03, 550.9, 51.4, 564.8]

IOU   = 87.26%

gt      = [62, 542.16, 1043, 574]

pred = [54.44, 542.16, 1064.44, 574.13]

IOU   = 96.74%

yticklabel:

bar:

IOU@0.9

Sensitivity of IOU

Our Proposed Model Output

Original Image

CV Proposals

Regression Targets

Model Output

After Postprocessing

Errors made by our model

Pre-processing

Ground-truth class and target offsets for RoIs

.

Finding neighbours and assigning links between them

Pre-processing

Observations

  1. 2-stage detectors work better than 1-stage detectors.
  2. The existing proposal methods cause many unnecessary computations.
  3. The anchors are independent  of the image.
  4. The way in which the target classes are assigned to the anchors can confuse the network.
  5. The regression targets (offsets) of the anchors are huge.
  6. In NMS, discard the proposals based on both localization and classification score rather than the classification score alone.
  7. IOU is not a good metric.
  8. mAP seems to be a misleading metric.

Observations 8

Observations

Model 1

Model 2

 # Proposals # Foreground  # Background # Neutral
FrRCNN_FPN_RA 225120 1838 222315 967
RetinaNet (FL) 225120 1914 224867 1661

For RetinaNet, class ids are directly assigned to the proposals

Observations 1 to 4

Observations

Foreground Proposals for FrRCNN_FPN_RA

Foreground Proposals for RetinaNet_FL

Background Proposals for RetinaNet_FL

Background Proposals for FrRCNN_FPN_RA

Observation 5: The regression targets (offsets) of the anchors is significantly large

Misalignment between classification confidence and localization accuracy

Non-monotonic localization in iterative bounding box regression

Observations 6: In NMS, discard the proposals based on both localization and classification score rather than classification score only

Non-monotonic localization in iterative bounding box regression

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
96.35% 96.51% 99.99% 82.72% 99.95% 100.00% 100.00% 99.94% 99.90% 99.99%
mAP @ 0.1
97.53%

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
95.84% 96.30% 99.99% 72.25% 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
mAP @ 0.5
96.43%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
94.30% 95.14% 99.96% 62.04% 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
mAP @ 0.75
92.98%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
85.54% 18.07% 93.68% 37.65% 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
mAP @ 0.9
72.29%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
96.35% 96.51% 99.99% 82.72% 99.95% 100.00% 100.00% 99.94% 99.90% 99.99%
mAP @ 0.1
97.53%

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
95.84% 96.30% 99.99% 72.25% 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
mAP @ 0.5
96.43%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
94.30% 95.14% 99.96% 62.04% 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
mAP @ 0.75
92.98%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
85.54% 18.07% 93.68% 37.65% 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
mAP @ 0.9
72.29%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
96.35% 96.51% 99.99% 82.72% 99.95% 100.00% 100.00% 99.94% 99.90% 99.99%
mAP @ 0.1
97.53%

FrRCNN_FPN_RA

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
95.84% 96.30% 99.99% 72.25% 99.95% 100.00% 99.99% 99.92% 99.90% 99.99%
mAP @ 0.5
96.43%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
94.30% 95.14% 99.96% 62.04% 99.94% 78.83% 99.99% 99.74% 99.90% 99.97%
mAP @ 0.75
92.98%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
85.54% 18.07% 93.68% 37.65% 96.30% 0.22% 99.09% 96.04% 99.46% 96.80%
mAP @ 0.9
72.29%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

YoloV3

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
76.92% 71.67% 92.13% 50.17% 94.44% 17.10% 90.59% 84.17% 80.90% 62.68%
mAP @ 0.75
72.08%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
15.51% 10.90% 7.15% 6.54% 11.70% 0.02% 4.39% 8.08% 9.59% 1.70%
mAP @ 0.9
7.56%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
87.95% 94.13% 99.95% 66.25% 99.94% 99.58% 99.99% 99.89% 99.77% 99.64%
mAP @ 0.5
94.71%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.04% 94.39% 99.96% 69.57% 99.97% 96.61% 99.99% 99.94% 99.80% 99.99%
mAP @ 0.1
95.63%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

YoloV3

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
76.92% 71.67% 92.13% 50.17% 94.44% 17.10% 90.59% 84.17% 80.90% 62.68%
mAP @ 0.75
72.08%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
15.51% 10.90% 7.15% 6.54% 11.70% 0.02% 4.39% 8.08% 9.59% 1.70%
mAP @ 0.9
7.56%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
87.95% 94.13% 99.95% 66.25% 99.94% 99.58% 99.99% 99.89% 99.77% 99.64%
mAP @ 0.5
94.71%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.04% 94.39% 99.96% 69.57% 99.97% 96.61% 99.99% 99.94% 99.80% 99.99%
mAP @ 0.1
95.63%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

YoloV3

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
76.92% 71.67% 92.13% 50.17% 94.44% 17.10% 90.59% 84.17% 80.90% 62.68%
mAP @ 0.75
72.08%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
15.51% 10.90% 7.15% 6.54% 11.70% 0.02% 4.39% 8.08% 9.59% 1.70%
mAP @ 0.9
7.56%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
87.95% 94.13% 99.95% 66.25% 99.94% 99.58% 99.99% 99.89% 99.77% 99.64%
mAP @ 0.5
94.71%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.04% 94.39% 99.96% 69.57% 99.97% 96.61% 99.99% 99.94% 99.80% 99.99%
mAP @ 0.1
95.63%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

MaskRCNN

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
81.99% 79.55% 99.34% 30.93% 95.70% 49.36% 97.75% 95.59% 99.42% 94.85%
mAP @ 0.75
82.45%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
47.54% 4.96% 50.83% 5.83% 32.43% 0.33% 46.2% 33.72% 80.53% 36.31%
mAP @ 0.9
33.86%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.24% 95.05% 99.77% 51.83% 99.87% 99.91% 99.94% 99.75% 99.97% 99.84%
mAP @ 0.5
93.72%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.76% 96.32% 99.77% 65.57% 99.91% 99.94% 99.95% 99.84% 99.97% 99.91%
mAP @ 0.1
95.50%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

MaskRCNN

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
81.99% 79.55% 99.34% 30.93% 95.70% 49.36% 97.75% 95.59% 99.42% 94.85%
mAP @ 0.75
82.45%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
47.54% 4.96% 50.83% 5.83% 32.43% 0.33% 46.2% 33.72% 80.53% 36.31%
mAP @ 0.9
33.86%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.24% 95.05% 99.77% 51.83% 99.87% 99.91% 99.94% 99.75% 99.97% 99.84%
mAP @ 0.5
93.72%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.76% 96.32% 99.77% 65.57% 99.91% 99.94% 99.95% 99.84% 99.97% 99.91%
mAP @ 0.1
95.50%

IOU=0.1

IOU=0.5

IOU=0.75

IOU=0.9

MaskRCNN

bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
81.99% 79.55% 99.34% 30.93% 95.70% 49.36% 97.75% 95.59% 99.42% 94.85%
mAP @ 0.75
82.45%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
47.54% 4.96% 50.83% 5.83% 32.43% 0.33% 46.2% 33.72% 80.53% 36.31%
mAP @ 0.9
33.86%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
91.24% 95.05% 99.77% 51.83% 99.87% 99.91% 99.94% 99.75% 99.97% 99.84%
mAP @ 0.5
93.72%
bar dotline legend-label line preview title xlabel xticklabel ylabel yticklabel
93.76% 96.32% 99.77% 65.57% 99.91% 99.94% 99.95% 99.84% 99.97% 99.91%
mAP @ 0.1
95.50%

Pre-processing

Adding CV-mask as 4th channel in the input image

Pre-processing

Adding CV-mask as 4th channel in the input image

Pre-processing

Adding CV-mask as 4th channel in the input image

Converting Line to Dotline using CV methods

Input Image

Detect xtickmarks

Harris Corner Detector

Markers

Gt-image

Pre-processing

Converting Line to Dotline using CV methods

Corner Detection Errors

Input Image

Detect xtickmarks

Harris Corner Detector

Markers

Gt-image

Converting Line to Dotline using CV methods

Ground-truth errors

Converting Line to Dotline using CV methods

mAP Error Analysis

CV Proposals (raw)

CV Proposals (refined)

(non-zero IoU with parent)

Region Proposal Analysis (CV based proposals)

CV Proposals

(IoU > 5% with parent)

Examples

Examples

Examples

  • FrRCNN_FPN_RA yticklabel errors
  • All ved models detections
  • FLOPs

(B) The y-value of the visual element could be wrong due to the following reasons: 

 

(a) pixel to value mapping is wrong i.e., the scale is wrong:

  1. the ticks using which scale is calculated are wrong due to incorrect OCR module
  2. the ticks using which scale is calculated are not the consecutive ticks (VED error)

(b) the height of the data element is wrong (VED error)

(A) The x-value of the visual element could be wrong due to the following reasons: 

 

(C) The z-value of the visual element could be wrong due to the following reasons: 

 

​

(a) the label associated with the bar is wrong because:

  1. the colour of the bar is not associated with the right preview
  2. mapping of preview and legend-label is not correct (VED error)
  3. OCR error in a legend-label text

(a) the tick-label associated with the bar is wrong (VED error)

(b) the OCR error in tick-label

TODO

  • Word Level OCR accuracy
  • Generate tables using detections at different IOUs

Optical Character Recognition (OCR)

Character Level Accuracy Word Level Accuracy Sentence Level Accuracy
99.49% 98.45% 98.29%
99.59% 98.39% 96.91%
99.80% 99.21% 99.15%
99.82% 99.29% 99.23%
94.31% 82.87% 54.90%
99.94% 99.77% 99.70%
98.82% 96.33% 91.36%
97.95% 94.60% 96.58%

Tesseract

Attention-OCR

Oracle Bounding Boxes

Textual Elements Character Level Accuracy Word Level Accuracy Sentence Level Accuracy
xlabel 99.94% 88.73% 78.64%
ylabel 98.43% 87.17% 79.58%
yticklabel 93.38% 86.92% 86.24%
xticklabel 94.8% 91.32% 90.95%
title 99.31% 97.92% 82.58%
legend-label 98.53% 95.29% 93.2%
Overall (avg.) 97.39% 91.22% 85.20%
(weighted avg.)

Optical Character Recognition (OCR)

Character Level Accuracy Word Level Accuracy Sentence Level Accuracy
94.61% 82.02% 81.44%
95.85% 83.27% 76.60%
85.20% 62.90% 61.0%
87.79% 62.33% 60.08%
83.55% 48.23% 10.0%
97.88% 92.33% 90.42%
90.81% 71.84% 63.25%
88.85% 65.97% 64.02%

Tesseract

Attention-OCR

Bounding Boxes after VED MaskRCNN)

Textual Elements Character Level Accuracy Word Level Accuracy Sentence Level Accuracy
xlabel 95.5% 81.29% 67.72%
ylabel 97.07% 82.8% 67.74%
yticklabel 88.07% 78.08% 77.13%
xticklabel 91.38% 83.81% 83.7%
title 94.6% 85.24% 34.13%
legend-label 91.99% 78.26% 79.71%
Overall (avg.) 93.10% 81.58% 68.35%
(weighted avg.)

SEMPRE

  • ADDITION: 66.77%
  • DIFFERENCE: 9.5%
  • MAX: 73.8%
  • MIN: 80.3%
  • RATIO: 0.68%
  • AVG: 67.33%
  • MEDIAN: 6.6%
  • COUNT: 41.85%
  • YES/NO: 0.00%
  • LOOKUP: 95.10%

Operation wise accuracy of SEMPRE

GT-GT-GT-SEMPRE

(32.55%)

VOES analysis

VED OCR SIE TQA
100% 100% 100% 32.55%
100% 100% 52.20% 26.24%
100% 100% 79.38% 29.24%
100% 97.06% 23.78%
94.21% 93.10% 20.22%

(SEMPRE)

(SIE)

(Tesseract OCR)

(VED)

SEMPRE on only OOV answer types gives 21.41%

bar: 82.81%

dot-line: 80.38%

line: 65.54%

(SIE after modifying rules)

Some interesting failure cases

SIE stage

TEST/2175.png

WACV 2020

By Nitesh Methani

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WACV 2020

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