This is an original Pytorch Implementation for our paper "EMCA: Efficient Multi-Scale Channel Attention Module"
Attention mechanisms have been explored with CNNs,both across the spatial and channel dimensions. However,all the existing methods devote the attention modules to cap-ture local interactions from a uni-scale. This paper tacklesthe following question: Can one consolidate multi-scale ag-gregation while learning channel attention more efficiently?To this end, we avail channel-wise attention over multi-ple feature scales, which empirically shows its aptitude toreplace the limited local and uni-scale attention modules.EMCA is lightweight and can efficiently model the globalcontext further it is easily integrated into any feed-forwardCNN architectures and trained in an end-to-end fashion. Wevalidate our novel architecture through comprehensive ex-periments on image classification, object detection and in-stance segmentation with different backbones. Our experi-ments show consistent gains in performances against theircounterparts, where our proposed module, named EMCA,outperforms other channel attention techniques in accuracyand latency trade-off. We also conduct experiments thatprobe the robustness of the learned representations.
2.1- Avoid Dense Integration Intuation:
2.2- Avoid Dense Integration Results:
Method
Model
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
SE
ECA
SRM
ALL
187
11.231
70.59
89.78
xx
192
11.148
70.75
89.74
xx
154
11.152
70.96
89.81
xx
First
R-18
204
11.189
70.91
89.96
xx
212
11.148
70.63
89.85
xx
165
11.150
71.31
90.07
xx
Last
204
11.189
70.92
89.83
xx
212
11.148
70.81
89.84
xx
165
11.150
71.04
90.00
xx
All
101
20.938
73.87
91.65
xx
107
20.788
74.13
91.68
xx
82
20.795
73.98
91.68
xx
First
R-34
122
20.829
73.84
91.64
xx
122
20.788
74.20
91.84
xx
96
20.790
74.51
91.91
xx
Last
122
20.829
73.64
91.49
xx
122
20.788
73.75
91.47
xx
96
20.790
73.63
91.44
xx
All
90
26.772
76.80
93.39
xx
87
24.373
77.12
93.68
xx
71
24.402
77.13
93.51
xx
First
R-50
97
25.037
76.56
93.28
xx
98
24.373
77.02
93.49
xx
81
24.380
76.98
93.41
xx
Last
97
25.037
75.71
92.60
xx
98
24.373
76.37
93.18
xx
81
24.380
76.73
93.26
xx
2.-1- Multi-Scale Inocrporation
2.2- Integrating EMCA Module:
3- HeatMap Visualization:
5- Top-1 Accuracy Visualization:
S
N`_i-j
Model
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
FPS
#.P (M)
Top-1(%)
Top-5(%)
Weights
SE
ECA
SRM
N/A
N/A
R-18
187
11.231
70.59
89.78
xx
192
11.148
70.75
89.74
xx
154
11.152
70.96
89.81
xx
0
0
204
11.189
70.91
89.96
xx
212
11.148
70.63
89.85
xx
165
11.150
71.31
90.07
xx
1
1
156
11.189
71.02
89.98
xx
174
11.148
70.83
89.96
xx
123
11.150
71.20
90.00
xx
1
N_i-j
160
11.190
71.00
90.00
xx
170
11.148
71.04
89.99
xx
113
11.150
71.02
90.00
xx
i-1
1
153
11.190
71.02
90.12
xx
169
11.148
70.59
89.78
xx
113
11.150
71.00
89.81
xx
N/A
N/A
R-34
101
20.938
73.87
91.65
xx
107
20.788
74.13
91.68
xx
82
20.795
73.98
91.68
xx
0
0
122
20.829
73.84
91.64
xx
122
20.788
74.20
91.84
xx
96
20.790
74.51
91.91
xx
1
1
109
20.829
74.33
91.89
xx
109
20.788
74.39
91.81
xx
82
20.790
74.39
91.77
xx
1
N_i-j
107
20.829
74.40
91.89
xx
107
20.788
74.46
91.70
xx
81
20.790
74.38
91.87
xx
i-1
1
103
20.829
74.02
91.74
xx
108
20.788
74.14
91.81
xx
80
20.790
74.57
91.90
xx
N/A
N/A
R-50
90
26.772
76.80
93.39
xx
87
24.373
77.12
xx
93.68
71
24.402
77.13
93.51
xx
0
0
97
25.037
76.56
93.28
xx
98
24.373
77.02
93.49
xx
81
24.380
76.98
93.41
xx
1
1
88
25.037
77.10
93.49
xx
94
24.373
76.98
93.55
xx
70
24.380
77.00
93.72
xx
1
N_i-j
90
25.037
77.33
93.52
xx
92
24.373
77.13
93.49
xx
70
24.380
77.20
93.54
xx
i-1
1
89
25.037
76.85
93.42
xx
91
24.373
76.82
93.41
xx
71
24.380
77.05
93.50
xx
S
N'_i-j
Model
FPS
#.P (M)
Top-1
Top-5
FPS
#.P (M)
Top-1
Top-5
FPS
#.P (M)
Top-1
Top-5
SE
ECA
SRM
N/A
N/A
R-18
187
11.231
70.59
89.78
192
11.148
70.75
89.74
154
11.152
70.96
89.81
0
0
204
11.189
70.91
89.96
212
11.148
70.63
89.85
165
11.150
71.31
90.07
1,
1
156
11.189
71.02
89.98
174
11.148
70.83
89.96
123
11.150
71.20
90.00
1
N_i-j
160
11.190
71.00
90.00
170
11.148
71.04
89.99
113
11.150
71.02
90.00
i-1
1
153
11.190
71.02
90.12
169
11.148
70.59
89.78
113
11.150
71.00
89.81
N/A,
N/A
R-34
101
20.938
73.87
91.65
107
20.788
74.13
91.68
82
20.795
73.98
91.68
0,
0
122
20.829
73.84
91.64
122
20.788
74.20
91.84
96
20.790
74.51
91.91
1,
1
109
20.829
74.33
91.89
109
20.788
74.39
91.81
82
20.790
74.39
91.77
1,
N_i-j
107
20.829
74.40
91.89
107
20.788
74.46
91.70
81
20.790
74.38
91.87
i-1,
1
103
20.829
74.02
91.74
108
20.788
74.14
91.81
80
20.790
74.57
91.90
N/A,
N/A
R-50
90
26.772
76.80
93.39
87
24.373
77.12
93.68
71
24.402
77.13
93.51
0,
0
97
25.037
76.56
93.28
98
24.373
77.02
93.49
81
24.380
76.98
93.41
1,
1
88
25.037
77.10
93.49
94
24.373
76.98
93.55
70
24.380
77.00
93.72
1,
N_i-j
90
25.037
77.33
93.52
92
24.373
77.13
93.49
70
24.380
77.20
93.54
i-1
1
89
25.037
76.85
93.42
91
24.373
76.82
93.41
71
24.380
77.05
93.50
Methods
Model
#.P (M)
GFLOPs
Top-1(RI)
Top-5
FPS
FPS*
FPS**
ResNet
R-18
11.148
1.694
70.40
89.45
270
23552
859
+SENet
11.231
1.695
70.59
89.78
187
21760
839
+EMCA-SE
11.190
1.695
71.00(215)
90.00
160
17313
813
+ECANet
11.148
1.695
70.78
89.92
192
22287
848
+ECANet*
11.148
1.695
70.75
89.74
192
22287
848
+EMCA-ECA
11.148
1.695
71.04(83)
89.99
170
19023
833
+SRM*
11.152
1.695
70.96
89.81
154
18794
823
+EMCA-SRM
11.150
1.694
71.02(10)
90.00
113
15190
803
ResNet
R-34
20.788
3.419
73.31
91.40
168
19712
840
+SENet
20.938
3.421
73.87
91.65
101
14279
805
+EMCA-SE
20.829
3.421
74.41 (96)
91.90
107
14372
812
+ECANet
20.788
3.420
74.21
91.83
107
14067
825
+ECANet*
20.788
3.420
74.13
91.68
107
14067
825
+EMCA-ECA
20.788
3.421
74.46 (40)
91.70
107
14080
822
+SRM*
20.795
3.419
73.98
91.68
82
12655
803
+EMCA-SRM
20.790
3.419
74.38 (59)
91.87
81
12579
795
ResNet
R-50
24.373
3.829
75.89
92.85
124
10032
668
+SENet
26.772
3.837
76.80
93.39
90
8156
597
+EMCA-SE
25.037
3.835
77.33 (58)
93.52
90
8099
589
+ECANet
24.373
3.834
77.48
93.68
87
8517
591
+ECANet *
24.373
3.834
77.12
93.68
87
8517
591
+EMCA-ECA
24.373
3.834
77.13 (1)
93.49
92
8615
600
+SRM *
24.402
3.829
77.13
93.51
71
6745
536
+EMCA-SRM
24.380
3.829
77.20 (6)
93.54
70
6698
532
Methods
Model
#.P (M)
GFLOPs
Top-1
Top-5
FPS
FPS*
FPS**
ResNet
R-18
11.148
1.694
70.40
89.45
270
23552
859
SENet
11.231
1.695
70.59
89.78
187
21760
839
ECANet*
11.148
1.695
70.75
89.74
192
22287
839
SRM*
11.152
1.694
70.96
89.81
154
18794
823
FCANet*
11.231
1.694
70.98
90.00
119
17680
808
BAM
11.712
1.821
75.98
92.82
91
7159
527
CBAM
11.234
1.695
70.73
89.91
104
8734
789
EMCA-ECA
11.148
1.695
71.04
89.99
170
19023
833
EMCA-SRM
11.150
1.694
71.02
90.00
113
15190
803
EMCA-SE
11.190
1.695
71.00
90.00
160
17313
813
ResNet
R-34
20.788
3.419
73.31
91.4
168
19712
840
SENet
20.938
3.421
73.87
91.65
101
14279
805
ECANet*
20.788
3.420
74.13
91.68
107
14067
825
SRM*
20.795
3.419
73.98
91.68
82
12655
803
FCANet*
20.938
3.419
74.18
91.75
87
13094
812
CBAM
20.943
3.420
74.01
91.76
59
12001
760
EMCA-ECA
20.788
3.421
74.46
91.70
107
14080
822
EMCA-SRM
20.790
3.419
74.38
91.87
81
12579
795
EMCA-SE
20.829
3.421
74.41
91.90
107
14372
812
ResNet
R-50
24.373
3.829
75.89
92.85
124
10032
668
SENet
26.772
3.837
76.80
93.39
90
8156
597
ECANet*
24.373
3.834
77.12
93.68
87
8517
591
SRM*
24.402
3.829
77.13
93.51
71
6745
536
FCANet*
26.772
3.831
77.27
93.70
74
7984
549
EPSANet*
21.517
3.373
77.31
93.72
28
802
388
SANet*
24.373
3.832
77.25
93.66
68
6670
406
A^2Nets
33.006
6.502
77.00
93.50
N/A
N/A
N/A
BAM
25.92
3.946
75.98
92.82
91
7159
527
CBAM
26.775
3.837
77.34
93.69
55
2460
208
EMCA-ECA
24.373
3.834
77.13
93.49
92
8615
600
EMCA-SRM
24.380
3.829
77.20
93.54
71
6698
532
EMCA-SE
25.037
3.835
77.33
93.52
90
8099
589
Methods
Detectors
#.P (M)
GFLOPs
AP
AP_50
AP_75
AP_S
AP_M
AP_L
ResNet-50
41.53
207.07
36.4
58.2
39.2
21.8
40.0
46.2
+SE
44.02
207.18
37.7
60.1
40.9
22.9
41.9
48.2
EMCA+SE
42.56
207.18
38.1
60.6
50.2
23.6
42.2
48.4
+ECA
41.53
207.18
38.0
60.6
40.9
23.4
42.1
48.0
+EMCA+ECA
Faster R-CNN
41.53
207.18
38.2
60.9
50.0
23.7
42.2
48.2
ResNet-50
44.18
275.58
37.2
58.9
40.3
22.2
40.7
48.0
+1 NL
46.50
288.70
38.0
59.8
41.0
N/A
N/A
N/A
+GC
46.90
279.60
39.4
61.6
42.4
N/A
N/A
N/A
+SE
46.67
275.69
38.7
60.9
42.1
23.4
42.7
50.0
+EMCA+SE
45.13
275.69
39.0
61.4
42.3
23.7
42.9
50.1
+ECA
44.18
275.69
39.0
61.3
42.1
24.2
42.8
49.9
+EMCA+ECA
Mask R-CNN
44.18
275.69
39.1
61.5
42.1
24.4
42.9
49.9
ResNet-50
37.74
239.32
35.6
55.5
38.2
20.0
39.6
46.8
+SE
40.23
239.43
37.1
57.2
39.9
21.2
40.7
49.3
+EMCA+SE
38.88
239.43
37.2
57.4
39.9
21.2
40.7
49.3
+ECA
37.74
239.43
37.3
57.7
39.6
21.9
41.3
48.9
+EMCA+ECA
RetinaNet
37.74
239.43
37.3
57.8
39.6
21.9
41.3
48.9
Methods
#.P (M)
GFLOPs
AP
AP_50
AP_75
AP_S
AP_M
AP_L
ResNet-50
44.18
275.58
34.1
55.5
36.2
16.1
36.7
50.0
+SE
46.67
275.69
35.4
57.4
37.8
17.1
38.6
51.8
+EMCA+SE
45.13
275.69
35.7
58.1
38.0
17.8
39.0
51.9
+ECA
44.18
275.69
35.6
58.1
37.7
17.6
39.0
51.8
+EMCA+ECA
44.18
275.69
35.7
58.4
37.7
17.9
39.1
51.9