premise string | hypothesis string | label int64 |
|---|---|---|
| | RunningTime(seconds) | Precision | Recall | F-measure |
| --- | --- | --- | --- | --- |
| P-CENI | 5877 | 0.083 | 0.557 | 0.145 | | | C-CENI | 5415 | 0.082 | 0.539 | 0.142 |
| --- | --- | --- | --- | --- |
| I-CENI | 7358 | 0.078 | 0.571 | 0.137 |
| MMRate | 21677 | 0.085 | 0.565 | 0.148 |
| NetRate | 14404 | 0.081 | 0.566 | 0.142 | | 1 |
| | RunningTime(seconds) | Precision | Recall | F-measure |
| --- | --- | --- | --- | --- |
| P-CENI | 5877 | 0.083 | 0.557 | 0.145 | | | | RunningTime(seconds) | Precision | Recall | F-measure |
| --- | --- | --- | --- | --- |
| P-CENI | 8182 | 0.058 | 0.140 | 0.082 |
| C-CENI | 7729 | 0.038 | 0.128 | 0.059 |
| I-CENI | 8559 | 0.043 | 0.233 | 0.073 |
| MMRate | 37328 | 0.046 | 0.144 | 0.070 |
| NetRate | 16461 | 0.045 | 0.168 | 0.071 | | 0 |
| | RunningTime(seconds) | Precision | Recall | F-measure |
| --- | --- | --- | --- | --- |
| P-CENI | 5877 | 0.083 | 0.557 | 0.145 |
| C-CENI | 5415 | 0.082 | 0.539 | 0.142 | | | I-CENI | 7358 | 0.078 | 0.571 | 0.137 |
| --- | --- | --- | --- | --- |
| MMRate | 21677 | 0.085 | 0.565 | 0.148 |
| NetRate | 14404 | 0.081 | 0.566 | 0.142 | | 1 |
| | RunningTime(seconds) | Precision | Recall | F-measure |
| --- | --- | --- | --- | --- |
| P-CENI | 5877 | 0.083 | 0.557 | 0.145 |
| C-CENI | 5415 | 0.082 | 0.539 | 0.142 | | | MMRate | 37328 | 0.046 | 0.144 | 0.070 |
| --- | --- | --- | --- | --- |
| NetRate | 16461 | 0.045 | 0.168 | 0.071 | | 0 |
| Method | G1 | G2 | G3 | G4 | Total |
| --- | --- | --- | --- | --- | --- |
| ML-CNN | 81.97 | 79.11 | 63.16 | 62.45 | 76.25 |
| CF | 81.00 | 82.08 | 77.63 | 78.50 | 80.48 |
| CRF | 85.00 | 84.33 | 81.25 | 82.50 | 83.95 |
| G-tasks | 90.10 | 92.29 | 89.80 | 73.52 | 89.18 | | | S-CNN | 90.50 | 92.90 | 87.00 | 89.57 | 90.43 |
| --- | --- | --- | --- | --- | --- |
| M-CNN | 91.72 | 94.26 | 87.96 | 91.51 | 91.70 |
| MG-CNN | 93.12 | 95.37 | 88.65 | 91.93 | 92.82 | | 1 |
| Method | G1 | G2 | G3 | G4 | Total |
| --- | --- | --- | --- | --- | --- |
| ML-CNN | 81.97 | 79.11 | 63.16 | 62.45 | 76.25 |
| CF | 81.00 | 82.08 | 77.63 | 78.50 | 80.48 |
| CRF | 85.00 | 84.33 | 81.25 | 82.50 | 83.95 |
| G-tasks | 90.10 | 92.29 | 89.80 | 73.52 | 89.18 | | | Method | G1 | G2 | G3 | G4 | Total |
| --- | --- | --- | --- | --- | --- |
| S-extract | 81.84 | 82.07 | 67.51 | 69.25 | 78.31 |
| M-extract | 84.98 | 89.89 | 81.41 | 81.03 | 85.29 |
| S-CNN | 90.50 | 92.90 | 87.00 | 89.57 | 90.43 |
| M-CNN | 91.72 | 94.26 | 87.96 | 91.51 | 91.70 |
| MG-CNN | 93.12 | 95.37 | 88.65 | 91.93 | 92.82 |
| CF | 81.00 | 82.08 | 77.63 | 78.50 | 80.48 |
| CRF | 85.00 | 84.33 | 81.25 | 82.50 | 83.95 | | 0 |
| Method | G1 | G2 | G3 | G4 | Total |
| --- | --- | --- | --- | --- | --- |
| ML-CNN | 81.97 | 79.11 | 63.16 | 62.45 | 76.25 | | | CF | 81.00 | 82.08 | 77.63 | 78.50 | 80.48 |
| --- | --- | --- | --- | --- | --- |
| CRF | 85.00 | 84.33 | 81.25 | 82.50 | 83.95 |
| G-tasks | 90.10 | 92.29 | 89.80 | 73.52 | 89.18 |
| S-CNN | 90.50 | 92.90 | 87.00 | 89.57 | 90.43 |
| M-CNN | 91.72 | 94.26 | 87.96 | 91.51 | 91.70 |
| MG-CNN | 93.12 | 95.37 | 88.65 | 91.93 | 92.82 | | 1 |
| Method | G1 | G2 | G3 | G4 | Total |
| --- | --- | --- | --- | --- | --- |
| ML-CNN | 81.97 | 79.11 | 63.16 | 62.45 | 76.25 | | | M-extract | 84.98 | 89.89 | 81.41 | 81.03 | 85.29 |
| --- | --- | --- | --- | --- | --- |
| S-CNN | 90.50 | 92.90 | 87.00 | 89.57 | 90.43 |
| M-CNN | 91.72 | 94.26 | 87.96 | 91.51 | 91.70 |
| MG-CNN | 93.12 | 95.37 | 88.65 | 91.93 | 92.82 |
| CF | 81.00 | 82.08 | 77.63 | 78.50 | 80.48 |
| CRF | 85.00 | 84.33 | 81.25 | 82.50 | 83.95 | | 0 |
| Surfaceexpression | Example | Weight |
| --- | --- | --- |
| Pronoun/zero-pronounwo(object)/ni(to)/kara(from) | (Johnni(to))shita(done). | 16 |
| Nounga(subject)/mo/da/nara | Johnga(subject)shita(do). | 15 | | | Nounwo(object)/ni/,/. | Johnni(object)shita(do). | 14 |
| --- | --- | --- |
| Nounhe(to)/de(in)/kara(from) | gakkou(school)he(to)iku(go). | 13 | | 1 |
| Surfaceexpression | Example | Weight |
| --- | --- | --- |
| Pronoun/zero-pronounwo(object)/ni(to)/kara(from) | (Johnni(to))shita(done). | 16 |
| Nounga(subject)/mo/da/nara | Johnga(subject)shita(do). | 15 | | | Surfaceexpression(Notincluding“wa”) | Example | Weight |
| --- | --- | --- |
| Pronoun/zero-pronounwo(object)/ni(to)/kara(from) | [Johnni(to)]shita(done). | 16 |
| Nounga(subject)/mo/da/nara/koso | Johnga(subject)shita(done). | 15 |
| Nounwo(object)/ni/,/. | Johnni(object)shita(done). | 14 |
| Nounhe(to)/de(in)/kara(from)/yori | gakkou(school)he(to)iku(go). | 13 | | 0 |
| Surfaceexpression | Example | Weight |
| --- | --- | --- |
| Pronoun/zero-pronounwo(object)/ni(to)/kara(from) | (Johnni(to))shita(done). | 16 | | | Nounga(subject)/mo/da/nara | Johnga(subject)shita(do). | 15 |
| --- | --- | --- |
| Nounwo(object)/ni/,/. | Johnni(object)shita(do). | 14 |
| Nounhe(to)/de(in)/kara(from) | gakkou(school)he(to)iku(go). | 13 | | 1 |
| Surfaceexpression | Example | Weight |
| --- | --- | --- |
| Pronoun/zero-pronounwo(object)/ni(to)/kara(from) | (Johnni(to))shita(done). | 16 | | | Nounwo(object)/ni/,/. | Johnni(object)shita(done). | 14 |
| --- | --- | --- |
| Nounhe(to)/de(in)/kara(from)/yori | gakkou(school)he(to)iku(go). | 13 | | 0 |
| Models | IOU | Params | GFLOPs |
| --- | --- | --- | --- |
| ERFNet | 70.45 | 2038448 | 27.705 |
| D* | 68.55 | 547120 | 10.597 |
| DG2* | 65.35 | 395568 | 8.852 |
| DG4* | 61.42 | 319792 | 7.980 |
| DG8* | 59.15 | 281904 | 7.543 |
| DG2S* | 65.36 | 395568 | 8.852 | | | DG4S* | 61.27 | 319792 | 7.980 |
| --- | --- | --- | --- |
| DG8S* | 59.89 | 281904 | 7.543 | | 1 |
| Models | IOU | Params | GFLOPs |
| --- | --- | --- | --- |
| ERFNet | 70.45 | 2038448 | 27.705 |
| D* | 68.55 | 547120 | 10.597 |
| DG2* | 65.35 | 395568 | 8.852 |
| DG4* | 61.42 | 319792 | 7.980 |
| DG8* | 59.15 | 281904 | 7.543 |
| DG2S* | 65.36 | 395568 | 8.852 | | | Models | IOU | Params | GFlops |
| --- | --- | --- | --- |
| D | 69.26 | 1291648 | 19.025 |
| DG2 | 69.71 | 1238960 | 18.998 |
| DG4 | 68.98 | 1202096 | 18.595 |
| DG8 | 69.57 | 1183664 | 18.394 |
| DG2S | 70.62 | 1238960 | 18.998 |
| DG4S | 69.59 | 1202096 | 18.595 |
| DG8S | 69.57 | 1183664 | 18.394 | | 0 |
| Models | IOU | Params | GFLOPs |
| --- | --- | --- | --- |
| ERFNet | 70.45 | 2038448 | 27.705 |
| D* | 68.55 | 547120 | 10.597 |
| DG2* | 65.35 | 395568 | 8.852 |
| DG4* | 61.42 | 319792 | 7.980 | | | DG8* | 59.15 | 281904 | 7.543 |
| --- | --- | --- | --- |
| DG2S* | 65.36 | 395568 | 8.852 |
| DG4S* | 61.27 | 319792 | 7.980 |
| DG8S* | 59.89 | 281904 | 7.543 | | 1 |
| Models | IOU | Params | GFLOPs |
| --- | --- | --- | --- |
| ERFNet | 70.45 | 2038448 | 27.705 |
| D* | 68.55 | 547120 | 10.597 |
| DG2* | 65.35 | 395568 | 8.852 |
| DG4* | 61.42 | 319792 | 7.980 | | | DG4 | 68.98 | 1202096 | 18.595 |
| --- | --- | --- | --- |
| DG8 | 69.57 | 1183664 | 18.394 |
| DG2S | 70.62 | 1238960 | 18.998 |
| DG4S | 69.59 | 1202096 | 18.595 |
| DG8S | 69.57 | 1183664 | 18.394 | | 0 |
| Feature | Type |
| --- | --- |
| Recency | ShallowLinguistic |
| Frequency | ShallowLinguistic |
| Grammaticalfunction | ShallowLinguistic |
| Previoussubject | ShallowLinguistic |
| Previousobject | ShallowLinguistic |
| PreviousREtype | ShallowLinguistic | | | Selectionalpreferences | Linguistic |
| --- | --- |
| Participanttypefit | Script |
| Predicateschemas | Script | | 1 |
| Feature | Type |
| --- | --- |
| Recency | ShallowLinguistic |
| Frequency | ShallowLinguistic |
| Grammaticalfunction | ShallowLinguistic |
| Previoussubject | ShallowLinguistic |
| Previousobject | ShallowLinguistic |
| PreviousREtype | ShallowLinguistic | | | LexicalFeatures |
| --- |
| currenttoken/currenttokenandnexttoken<br>lengthoftheDA<br>isdigit?<br>appearinginnextDA?<br>nextDAisapositivefeedback? |
| StructuralFeatures |
| seeTable3 |
| GrammaticalFeatures |
| part-of-speech<br>phrasetype(VP/NP/PP)<br>dependencyrelations |
| OtherFeatures |
| speakerrole<br>topic | | 0 |
| Feature | Type |
| --- | --- |
| Recency | ShallowLinguistic |
| Frequency | ShallowLinguistic |
| Grammaticalfunction | ShallowLinguistic |
| Previoussubject | ShallowLinguistic | | | Previousobject | ShallowLinguistic |
| --- | --- |
| PreviousREtype | ShallowLinguistic |
| Selectionalpreferences | Linguistic |
| Participanttypefit | Script |
| Predicateschemas | Script | | 1 |
| Feature | Type |
| --- | --- |
| Recency | ShallowLinguistic |
| Frequency | ShallowLinguistic |
| Grammaticalfunction | ShallowLinguistic |
| Previoussubject | ShallowLinguistic | | | part-of-speech<br>phrasetype(VP/NP/PP)<br>dependencyrelations |
| --- |
| OtherFeatures |
| speakerrole<br>topic | | 0 |
| Model | ItautecMX214 |
| --- | --- |
| CPU | 2xIntelXeonX56753.07GHz |
| DRAM | 48GBDDR31333MHz |
| Disk | 5.8TBSATA3GB/s |
| QPI | 6,4GT/s |
| S.O. | Ubuntu15.04 |
| Kernel | 3.19.0-15 | | | Hypervisor | KVM |
| --- | --- |
| HardwareEmulation | Qemu2.2.0 | | 1 |
| Model | ItautecMX214 |
| --- | --- |
| CPU | 2xIntelXeonX56753.07GHz |
| DRAM | 48GBDDR31333MHz |
| Disk | 5.8TBSATA3GB/s |
| QPI | 6,4GT/s |
| S.O. | Ubuntu15.04 |
| Kernel | 3.19.0-15 | | | ItemType | ItemDescription | Qty |
| --- | --- | --- |
| Chassis | NEXXUS4080ALDesk-sideChassis | 1 |
| ComputeShelve | ComputeShelveAL/EN(IncludingPowerSupply) | 4 |
| Motherboard | IntelS5000ALMotherboard-1333MHzFSB | 4 |
| Processor | XeonQuad-CoreE54102.3312M1333MHz-80W | 8 |
| MemoryType1 | 2GBFBDIMMSDDR2667ECC/Reg. | 32 |
| HardDrive | HDDRaid500GB,7200rpm,16MB,SATAIINCQ | 2 |
| HardDrive | HDDRaid250GB,7200rpm,16MB,SATAIINCQ | 3 |
| HeatSink | HighPerformancePassiveHeatSink | 8 |
| RiserCard | PCI/ExpressRiserCard | 4 |
| KVM/USB | IntegratedKVM/USBSwitch | 1 |
| GigabitSwitch | Integrated16PortGigESwitch | 1 |
| PowerCord | PowerCordUSA/JPN15AmpROHSCompliant | 1 | | 0 |
| Model | ItautecMX214 |
| --- | --- |
| CPU | 2xIntelXeonX56753.07GHz |
| DRAM | 48GBDDR31333MHz |
| Disk | 5.8TBSATA3GB/s |
| QPI | 6,4GT/s |
| S.O. | Ubuntu15.04 | | | Kernel | 3.19.0-15 |
| --- | --- |
| Hypervisor | KVM |
| HardwareEmulation | Qemu2.2.0 | | 1 |
| Model | ItautecMX214 |
| --- | --- |
| CPU | 2xIntelXeonX56753.07GHz |
| DRAM | 48GBDDR31333MHz |
| Disk | 5.8TBSATA3GB/s |
| QPI | 6,4GT/s |
| S.O. | Ubuntu15.04 | | | HardDrive | HDDRaid500GB,7200rpm,16MB,SATAIINCQ | 2 |
| --- | --- | --- |
| HardDrive | HDDRaid250GB,7200rpm,16MB,SATAIINCQ | 3 |
| HeatSink | HighPerformancePassiveHeatSink | 8 |
| RiserCard | PCI/ExpressRiserCard | 4 |
| KVM/USB | IntegratedKVM/USBSwitch | 1 |
| GigabitSwitch | Integrated16PortGigESwitch | 1 |
| PowerCord | PowerCordUSA/JPN15AmpROHSCompliant | 1 | | 0 |
| Parameter | Value |
| --- | --- |
| Stackedautoencoderinputlayerdim. | 1001 |
| Stackedautoencodersecondlayerdim. | 91 | | | Stackedautoencoderbottlenecklayerdim. | 21 |
| --- | --- |
| Numberofhiddenlayers?? | 3 |
| Firstlayeractivation | tanh |
| Hiddenlayeractivation | sigmoid |
| InitialstatesinHMM | 12-18 |
| NumberofGMMcomponents | 2-5 |
| MinimumdurationofHMMstates | 0.2s-1s |
| Splicingcontext(past) | 5frames |
| Splicingcontext(future) | 5frames |
| Features | MFCC |
| Windowlength | 25ms |
| Skip-rate | 10ms |
| Samplingrate | 8000Hz | | 1 |
| Parameter | Value |
| --- | --- |
| Stackedautoencoderinputlayerdim. | 1001 |
| Stackedautoencodersecondlayerdim. | 91 | | | LayerType | Parameters |
| --- | --- |
| Input | 94x24pixelsRGBimage |
| Convolution | #643x3stride1 |
| MaxPooling | #643x3stride1 |
| Smallbasicblock | #1283x3stride1 |
| MaxPooling | #643x3stride(2,1) |
| Smallbasicblock | #2563x3stride1 |
| Smallbasicblock | #2563x3stride1 |
| MaxPooling | #643x3stride(2,1) |
| Dropout | 0.5ratio |
| Convolution | #2564x1stride1 |
| Dropout | 0.5ratio |
| Convolution | #classnumber1x13stride1 | | 0 |
| Parameter | Value |
| --- | --- |
| Stackedautoencoderinputlayerdim. | 1001 |
| Stackedautoencodersecondlayerdim. | 91 | | | Stackedautoencoderbottlenecklayerdim. | 21 |
| --- | --- |
| Numberofhiddenlayers?? | 3 |
| Firstlayeractivation | tanh |
| Hiddenlayeractivation | sigmoid |
| InitialstatesinHMM | 12-18 |
| NumberofGMMcomponents | 2-5 |
| MinimumdurationofHMMstates | 0.2s-1s |
| Splicingcontext(past) | 5frames |
| Splicingcontext(future) | 5frames |
| Features | MFCC |
| Windowlength | 25ms |
| Skip-rate | 10ms |
| Samplingrate | 8000Hz | | 1 |
| Parameter | Value |
| --- | --- |
| Stackedautoencoderinputlayerdim. | 1001 |
| Stackedautoencodersecondlayerdim. | 91 | | | Convolution | #2564x1stride1 |
| --- | --- |
| Dropout | 0.5ratio |
| Convolution | #classnumber1x13stride1 | | 0 |
| \|Λ\|Algorithm | Objectivevaluesfordifferentp | | | |
| --- | --- | --- | --- | --- |
| p=0.9 | p=0.6 | p=0.5 | p=0.3 | p=0.1 | | | 10DCG<br>10Greedy | 7.1<br>6.8 | 5.2<br>5.1 | 4.7<br>4.6 | 3.4<br>3.4 | 2.6<br>2.6 |
| --- | --- | --- | --- | --- | --- |
| 50DCG<br>50Greedy | 7.4<br>6.82 | 5.84<br>5.66 | 5.18<br>5.06 | 3.98<br>3.98 | 2.68<br>2.68 |
| 100DCG<br>100Greedy | 7.38<br>6.69 | 5.61<br>5.52 | 5.04<br>5.04 | 3.96<br>3.96 | 2.76<br>2.76 | | 1 |
| \|Λ\|Algorithm | Objectivevaluesfordifferentp | | | |
| --- | --- | --- | --- | --- |
| p=0.9 | p=0.6 | p=0.5 | p=0.3 | p=0.1 | | | Pattern<br>Size | Algorithm | System1 | System2 | System3 |
| --- | --- | --- | --- | --- |
| 2 | ABM<br>HAL<br>L<br>SF<br>TBM | 8.89665<br>8.26117<br>6.08718<br>4.28357<br>10.5142 | 24.6946<br>24.6946<br>24.6946<br>9.87784<br>32.9261 | 32.9261<br>32.9261<br>32.9261<br>24.6946<br>32.9261 |
| 4 | ABM<br>HAL<br>L<br>SF<br>TBM | 20.4425<br>23.3995<br>6.52724<br>4.29622<br>21.2602 | 46.7838<br>51.0369<br>27.8712<br>9.84923<br>49.123 | 68.9446<br>83.6137<br>38.9093<br>23.3919<br>71.4517 |
| 6 | ABM<br>HAL<br>L<br>SF<br>TBM | 28.1637<br>31.2569<br>6.45279<br>4.28142<br>29.2294 | 60.2832<br>63.6323<br>27.4015<br>9.84005<br>62.249 | 89.4829<br>108.055<br>37.9265<br>22.1973<br>93.8837 |
| 8 | ABM<br>HAL<br>L<br>SF<br>TBM | 33.7463<br>37.0999<br>6.34086<br>4.23323<br>35.3437 | 69.2828<br>73.0482<br>26.6684<br>9.78229<br>72.2627 | 106.674<br>126.801<br>36.5241<br>22.0342<br>112.007 |
| 10 | ABM<br>HAL<br>L<br>SF<br>TBM | 39.6329<br>42.5986<br>6.32525<br>4.22537<br>41.1973 | 76.2308<br>80.5134<br>26.6383<br>9.74924<br>78.7439 | 117.47<br>135.202<br>36.1904<br>21.9134<br>125.714 |
| 14 | ABM<br>HAL<br>L<br>SF<br>TBM | 47.7986<br>49.8997<br>6.22037<br>4.189<br>49.3573 | 89.1214<br>92.9962<br>25.9262<br>9.72233<br>92.9962 | 129.631<br>147.511<br>33.6837<br>21.1774<br>142.594 |
| 18 | ABM<br>HAL<br>L<br>SF<br>TBM | 50.1514<br>50.1514<br>5.86185<br>4.05173<br>51.2912 | 97.859<br>101.773<br>24.7023<br>9.63763<br>97.859 | 141.352<br>159.021<br>31.4115<br>21.0275<br>149.667 | | 0 |
| \|Λ\|Algorithm | Objectivevaluesfordifferentp | | | |
| --- | --- | --- | --- | --- |
| p=0.9 | p=0.6 | p=0.5 | p=0.3 | p=0.1 | | | 10DCG<br>10Greedy | 7.1<br>6.8 | 5.2<br>5.1 | 4.7<br>4.6 | 3.4<br>3.4 | 2.6<br>2.6 |
| --- | --- | --- | --- | --- | --- |
| 50DCG<br>50Greedy | 7.4<br>6.82 | 5.84<br>5.66 | 5.18<br>5.06 | 3.98<br>3.98 | 2.68<br>2.68 |
| 100DCG<br>100Greedy | 7.38<br>6.69 | 5.61<br>5.52 | 5.04<br>5.04 | 3.96<br>3.96 | 2.76<br>2.76 | | 1 |
| \|Λ\|Algorithm | Objectivevaluesfordifferentp | | | |
| --- | --- | --- | --- | --- |
| p=0.9 | p=0.6 | p=0.5 | p=0.3 | p=0.1 | | | 8 | ABM<br>HAL<br>L<br>SF<br>TBM | 33.7463<br>37.0999<br>6.34086<br>4.23323<br>35.3437 | 69.2828<br>73.0482<br>26.6684<br>9.78229<br>72.2627 | 106.674<br>126.801<br>36.5241<br>22.0342<br>112.007 |
| --- | --- | --- | --- | --- |
| 10 | ABM<br>HAL<br>L<br>SF<br>TBM | 39.6329<br>42.5986<br>6.32525<br>4.22537<br>41.1973 | 76.2308<br>80.5134<br>26.6383<br>9.74924<br>78.7439 | 117.47<br>135.202<br>36.1904<br>21.9134<br>125.714 |
| 14 | ABM<br>HAL<br>L<br>SF<br>TBM | 47.7986<br>49.8997<br>6.22037<br>4.189<br>49.3573 | 89.1214<br>92.9962<br>25.9262<br>9.72233<br>92.9962 | 129.631<br>147.511<br>33.6837<br>21.1774<br>142.594 |
| 18 | ABM<br>HAL<br>L<br>SF<br>TBM | 50.1514<br>50.1514<br>5.86185<br>4.05173<br>51.2912 | 97.859<br>101.773<br>24.7023<br>9.63763<br>97.859 | 141.352<br>159.021<br>31.4115<br>21.0275<br>149.667 | | 0 |
| Models | model1 | model2 | model3 |
| --- | --- | --- | --- |
| Trainingloss | 0.82 | 1.01 | 1.72 | | | Numberofepochs | 50 | 30 | 19 |
| --- | --- | --- | --- |
| BLEUscore | 24.5/9.0/3.2/1.3 | 26.0/9.7/3.6/1.5 | 26.4/10.1/3.8/1.6 |
| METEORscore | 23.0 | 23.9 | 23.9 | | 1 |
| Models | model1 | model2 | model3 |
| --- | --- | --- | --- |
| Trainingloss | 0.82 | 1.01 | 1.72 | | | Model | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| c5 | c40 | c5 | c40 | c5 | c40 | c5 | c40 | c5 | c40 | c5 | |
| SCN-LSTM | 0.740 | 0.917 | 0.575 | 0.839 | 0.436 | 0.739 | 0.331 | 0.631 | 0.257 | 0.348 | 0.543 |
| ATT | 0.731 | 0.900 | 0.565 | 0.815 | 0.424 | 0.709 | 0.316 | 0.599 | 0.250 | 0.335 | 0.535 |
| OV | 0.713 | 0.895 | 0.542 | 0.802 | 0.407 | 0.694 | 0.309 | 0.587 | 0.254 | 0.346 | 0.530 |
| MSRCap | 0.715 | 0.907 | 0.543 | 0.819 | 0.407 | 0.710 | 0.308 | 0.601 | 0.248 | 0.339 | 0.526 | | 0 |
| Models | model1 | model2 | model3 |
| --- | --- | --- | --- |
| Trainingloss | 0.82 | 1.01 | 1.72 |
| Numberofepochs | 50 | 30 | 19 | | | BLEUscore | 24.5/9.0/3.2/1.3 | 26.0/9.7/3.6/1.5 | 26.4/10.1/3.8/1.6 |
| --- | --- | --- | --- |
| METEORscore | 23.0 | 23.9 | 23.9 | | 1 |
| Models | model1 | model2 | model3 |
| --- | --- | --- | --- |
| Trainingloss | 0.82 | 1.01 | 1.72 |
| Numberofepochs | 50 | 30 | 19 | | | ATT | 0.731 | 0.900 | 0.565 | 0.815 | 0.424 | 0.709 | 0.316 | 0.599 | 0.250 | 0.335 | 0.535 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OV | 0.713 | 0.895 | 0.542 | 0.802 | 0.407 | 0.694 | 0.309 | 0.587 | 0.254 | 0.346 | 0.530 |
| MSRCap | 0.715 | 0.907 | 0.543 | 0.819 | 0.407 | 0.710 | 0.308 | 0.601 | 0.248 | 0.339 | 0.526 | | 0 |
| | MetaCREST-01 | CREST-Base | CREST-10 | CREST-05 | CREST-03 | CREST-01 |
| --- | --- | --- | --- | --- | --- | --- |
| bag | 0.45 | 0.45 | 0.45 | 0.41 | 0.37 | 0.31 |
| ball1 | 0.8 | 0.8 | 0.8 | 0.81 | 0.8 | 0.8 |
| ball2 | 0.44 | 0.31 | 0.38 | 0.3 | 0.47 | 0.45 |
| basketball | 0.6 | 0.55 | 0.55 | 0.6 | 0.62 | 0.61 |
| birds1 | 0.52 | 0.53 | 0.52 | 0.5 | 0.53 | 0.53 |
| birds2 | 0.29 | 0.28 | 0.32 | 0.28 | 0.28 | 0.28 |
| blanket | 0.58 | 0.61 | 0.61 | 0.64 | 0.62 | 0.61 |
| bmx | 0.24 | 0.42 | 0.42 | 0.42 | 0.43 | 0.42 |
| bolt1 | 0.52 | 0.66 | 0.67 | 0.67 | 0.68 | 0.68 |
| bolt2 | 0.56 | 0.48 | 0.57 | 0.58 | 0.58 | 0.57 |
| book | 0.41 | 0.39 | 0.38 | 0.39 | 0.37 | 0.46 |
| butterfly | 0.45 | 0.4 | 0.4 | 0.41 | 0.4 | 0.41 |
| car1 | 0.74 | 0.74 | 0.75 | 0.76 | 0.75 | 0.74 |
| car2 | 0.77 | 0.73 | 0.74 | 0.75 | 0.77 | 0.78 |
| crossing | 0.71 | 0.69 | 0.69 | 0.7 | 0.71 | 0.71 |
| dinosaur | 0.41 | 0.29 | 0.28 | 0.32 | 0.32 | 0.43 |
| fernando | 0.38 | 0.35 | 0.32 | 0.4 | 0.41 | 0.42 |
| fish1 | 0.43 | 0.4 | 0.42 | 0.53 | 0.52 | 0.52 |
| fish2 | 0.34 | 0.42 | 0.39 | 0.34 | 0.34 | 0.33 |
| fish3 | 0.6 | 0.61 | 0.61 | 0.59 | 0.58 | 0.58 |
| fish4 | 0.46 | 0.43 | 0.31 | 0.42 | 0.43 | 0.43 |
| girl | 0.63 | 0.68 | 0.68 | 0.69 | 0.69 | 0.69 |
| glove | 0.52 | 0.52 | 0.53 | 0.51 | 0.53 | 0.55 |
| godfather | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 |
| graduate | 0.38 | 0.47 | 0.48 | 0.49 | 0.46 | 0.47 |
| gymnastics1 | 0.37 | 0.38 | 0.36 | 0.37 | 0.39 | 0.39 |
| gymnastics2 | 0.5 | 0.5 | 0.49 | 0.52 | 0.5 | 0.5 |
| gymnastics3 | 0.29 | 0.39 | 0.35 | 0.37 | 0.33 | 0.29 |
| gymnastics4 | 0.5 | 0.44 | 0.43 | 0.42 | 0.43 | 0.44 |
| hand | 0.43 | 0.4 | 0.38 | 0.42 | 0.41 | 0.44 |
| handball1 | 0.6 | 0.57 | 0.6 | 0.62 | 0.62 | 0.57 |
| handball2 | 0.46 | 0.49 | 0.52 | 0.52 | 0.54 | 0.44 |
| helicopter | 0.38 | 0.51 | 0.51 | 0.49 | 0.47 | 0.47 |
| iceskater1 | 0.51 | 0.52 | 0.52 | 0.56 | 0.56 | 0.54 |
| iceskater2 | 0.58 | 0.53 | 0.53 | 0.53 | 0.55 | 0.54 |
| leaves | 0.39 | 0.13 | 0.13 | 0.3 | 0.3 | 0.29 |
| marching | 0.74 | 0.75 | 0.75 | 0.74 | 0.74 | 0.75 |
| matrix | 0.66 | 0.55 | 0.55 | 0.61 | 0.61 | 0.54 |
| motocross1 | 0.47 | 0.42 | 0.42 | 0.42 | 0.39 | 0.39 | | | motocross2 | 0.45 | 0.42 | 0.42 | 0.49 | 0.48 | 0.54 |
| --- | --- | --- | --- | --- | --- | --- |
| nature | 0.46 | 0.3 | 0.3 | 0.32 | 0.29 | 0.32 |
| octopus | 0.4 | 0.32 | 0.33 | 0.44 | 0.43 | 0.37 |
| pedestrian1 | 0.73 | 0.68 | 0.72 | 0.7 | 0.69 | 0.68 |
| pedestrian2 | 0.3 | 0.45 | 0.49 | 0.38 | 0.37 | 0.34 |
| rabbit | 0.41 | 0.23 | 0.33 | 0.32 | 0.41 | 0.29 |
| racing | 0.52 | 0.42 | 0.42 | 0.4 | 0.4 | 0.41 |
| road | 0.58 | 0.58 | 0.57 | 0.62 | 0.64 | 0.67 |
| shaking | 0.66 | 0.64 | 0.69 | 0.73 | 0.76 | 0.68 |
| sheep | 0.49 | 0.5 | 0.5 | 0.5 | 0.52 | 0.53 |
| singer1 | 0.6 | 0.65 | 0.68 | 0.61 | 0.57 | 0.41 |
| singer2 | 0.69 | 0.65 | 0.6 | 0.54 | 0.66 | 0.64 |
| singer3 | 0.4 | 0.23 | 0.25 | 0.16 | 0.15 | 0.15 |
| soccer1 | 0.52 | 0.48 | 0.53 | 0.53 | 0.55 | 0.51 |
| soccer2 | 0.56 | 0.6 | 0.57 | 0.57 | 0.57 | 0.57 |
| soldier | 0.39 | 0.37 | 0.36 | 0.36 | 0.37 | 0.4 |
| sphere | 0.49 | 0.46 | 0.44 | 0.41 | 0.39 | 0.41 |
| tiger | 0.71 | 0.68 | 0.68 | 0.67 | 0.68 | 0.66 |
| traffic | 0.79 | 0.79 | 0.8 | 0.8 | 0.76 | 0.73 |
| tunnel | 0.52 | 0.67 | 0.68 | 0.43 | 0.41 | 0.38 |
| wiper | 0.62 | 0.65 | 0.65 | 0.64 | 0.65 | 0.65 |
| mean | 0.51 | 0.5 | 0.51 | 0.51 | 0.51 | 0.5 |
| | | | | | | | | 1 |
| | MetaCREST-01 | CREST-Base | CREST-10 | CREST-05 | CREST-03 | CREST-01 |
| --- | --- | --- | --- | --- | --- | --- |
| bag | 0.45 | 0.45 | 0.45 | 0.41 | 0.37 | 0.31 |
| ball1 | 0.8 | 0.8 | 0.8 | 0.81 | 0.8 | 0.8 |
| ball2 | 0.44 | 0.31 | 0.38 | 0.3 | 0.47 | 0.45 |
| basketball | 0.6 | 0.55 | 0.55 | 0.6 | 0.62 | 0.61 |
| birds1 | 0.52 | 0.53 | 0.52 | 0.5 | 0.53 | 0.53 |
| birds2 | 0.29 | 0.28 | 0.32 | 0.28 | 0.28 | 0.28 |
| blanket | 0.58 | 0.61 | 0.61 | 0.64 | 0.62 | 0.61 |
| bmx | 0.24 | 0.42 | 0.42 | 0.42 | 0.43 | 0.42 |
| bolt1 | 0.52 | 0.66 | 0.67 | 0.67 | 0.68 | 0.68 |
| bolt2 | 0.56 | 0.48 | 0.57 | 0.58 | 0.58 | 0.57 |
| book | 0.41 | 0.39 | 0.38 | 0.39 | 0.37 | 0.46 |
| butterfly | 0.45 | 0.4 | 0.4 | 0.41 | 0.4 | 0.41 |
| car1 | 0.74 | 0.74 | 0.75 | 0.76 | 0.75 | 0.74 |
| car2 | 0.77 | 0.73 | 0.74 | 0.75 | 0.77 | 0.78 |
| crossing | 0.71 | 0.69 | 0.69 | 0.7 | 0.71 | 0.71 |
| dinosaur | 0.41 | 0.29 | 0.28 | 0.32 | 0.32 | 0.43 |
| fernando | 0.38 | 0.35 | 0.32 | 0.4 | 0.41 | 0.42 |
| fish1 | 0.43 | 0.4 | 0.42 | 0.53 | 0.52 | 0.52 |
| fish2 | 0.34 | 0.42 | 0.39 | 0.34 | 0.34 | 0.33 |
| fish3 | 0.6 | 0.61 | 0.61 | 0.59 | 0.58 | 0.58 |
| fish4 | 0.46 | 0.43 | 0.31 | 0.42 | 0.43 | 0.43 |
| girl | 0.63 | 0.68 | 0.68 | 0.69 | 0.69 | 0.69 |
| glove | 0.52 | 0.52 | 0.53 | 0.51 | 0.53 | 0.55 |
| godfather | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 |
| graduate | 0.38 | 0.47 | 0.48 | 0.49 | 0.46 | 0.47 |
| gymnastics1 | 0.37 | 0.38 | 0.36 | 0.37 | 0.39 | 0.39 |
| gymnastics2 | 0.5 | 0.5 | 0.49 | 0.52 | 0.5 | 0.5 |
| gymnastics3 | 0.29 | 0.39 | 0.35 | 0.37 | 0.33 | 0.29 |
| gymnastics4 | 0.5 | 0.44 | 0.43 | 0.42 | 0.43 | 0.44 |
| hand | 0.43 | 0.4 | 0.38 | 0.42 | 0.41 | 0.44 |
| handball1 | 0.6 | 0.57 | 0.6 | 0.62 | 0.62 | 0.57 |
| handball2 | 0.46 | 0.49 | 0.52 | 0.52 | 0.54 | 0.44 |
| helicopter | 0.38 | 0.51 | 0.51 | 0.49 | 0.47 | 0.47 |
| iceskater1 | 0.51 | 0.52 | 0.52 | 0.56 | 0.56 | 0.54 |
| iceskater2 | 0.58 | 0.53 | 0.53 | 0.53 | 0.55 | 0.54 |
| leaves | 0.39 | 0.13 | 0.13 | 0.3 | 0.3 | 0.29 |
| marching | 0.74 | 0.75 | 0.75 | 0.74 | 0.74 | 0.75 |
| matrix | 0.66 | 0.55 | 0.55 | 0.61 | 0.61 | 0.54 |
| motocross1 | 0.47 | 0.42 | 0.42 | 0.42 | 0.39 | 0.39 | | | | MetaCREST-01 | CREST-Base | CREST-10 | CREST-05 | CREST-03 | CREST-01 |
| --- | --- | --- | --- | --- | --- | --- |
| bag | 0 | 0 | 0 | 0 | 0 | 0 |
| ball1 | 0 | 0 | 0 | 0 | 0 | 0 |
| ball2 | 1 | 0 | 0 | 0 | 1 | 1 |
| basketball | 1 | 2 | 1.9 | 1 | 1 | 1 |
| birds1 | 1 | 1 | 2 | 2 | 1 | 1 |
| birds2 | 0 | 0 | 1 | 0 | 0 | 0 |
| blanket | 0 | 0 | 0 | 0 | 0 | 0 |
| bmx | 1 | 0 | 0 | 0 | 0 | 0 |
| bolt1 | 0 | 1 | 1.1 | 2 | 2 | 2 |
| bolt2 | 0 | 0 | 1 | 1 | 1 | 2 |
| book | 2 | 4 | 6 | 3 | 3.2 | 5 |
| butterfly | 0 | 1 | 1 | 1 | 1 | 1 |
| car1 | 1 | 1 | 1 | 1 | 1 | 1 |
| car2 | 0 | 0 | 0 | 0 | 0 | 0 |
| crossing | 1 | 1 | 1 | 1 | 1 | 1 |
| dinosaur | 3 | 4 | 4 | 3 | 3 | 3 |
| fernando | 0.87 | 1 | 0 | 1 | 1 | 1 |
| fish1 | 2.6 | 3 | 3.2 | 2 | 2 | 2 |
| fish2 | 3.93 | 4 | 4 | 5 | 4 | 4 |
| fish3 | 0 | 0 | 0 | 0 | 0 | 0 |
| fish4 | 0 | 0 | 2 | 0.6 | 0.2 | 0 |
| girl | 1 | 1 | 1 | 1 | 1 | 1 |
| glove | 2 | 2 | 2 | 2 | 2 | 2 |
| godfather | 0 | 0 | 0 | 0 | 0 | 0 |
| graduate | 0 | 3 | 3.2 | 1.1 | 1.3 | 3 |
| gymnastics1 | 1.67 | 1 | 2.9 | 2.3 | 5.2 | 5.1 |
| gymnastics2 | 3 | 2 | 2 | 3 | 2 | 2 |
| gymnastics3 | 1.53 | 3 | 2.9 | 2 | 3 | 3 |
| gymnastics4 | 0 | 0 | 0 | 0 | 0 | 0 |
| hand | 7 | 8 | 7 | 7 | 7 | 6 |
| handball1 | 0 | 1 | 0 | 0 | 0 | 0 |
| handball2 | 3.27 | 5 | 3.4 | 3 | 3 | 2.9 |
| helicopter | 0 | 1 | 1 | 1 | 1 | 1 |
| iceskater1 | 0 | 0 | 0 | 0 | 0 | 0 |
| iceskater2 | 0.8 | 2 | 2.2 | 1.4 | 1.2 | 1.3 |
| leaves | 1 | 3 | 3 | 3 | 3 | 3 |
| marching | 0 | 0 | 0 | 0 | 0 | 0 |
| matrix | 1 | 2 | 2 | 2 | 2 | 1 |
| motocross1 | 0.2 | 3 | 3 | 3 | 3 | 3 |
| motocross2 | 0 | 1 | 0.8 | 0.3 | 0.9 | 0 |
| nature | 1.93 | 4 | 4 | 4 | 4 | 4 |
| octopus | 0 | 0 | 0 | 1 | 1 | 0 |
| pedestrian1 | 1.87 | 2 | 1.2 | 1 | 1 | 2 |
| pedestrian2 | 0 | 1 | 1 | 1 | 1 | 1 |
| rabbit | 3 | 5 | 4 | 4.2 | 3 | 4.1 |
| racing | 0 | 0 | 0 | 0 | 0 | 0 |
| road | 0 | 0 | 0 | 0 | 0 | 0 |
| shaking | 0 | 0 | 1 | 1 | 1 | 0 |
| sheep | 0 | 0 | 0 | 0 | 0 | 0 |
| singer1 | 0 | 0 | 0 | 0 | 0 | 0 |
| singer2 | 0 | 1 | 1 | 2 | 1 | 1 |
| singer3 | 1.93 | 0 | 0 | 1 | 1 | 1 |
| soccer1 | 2.93 | 3 | 2 | 2 | 1 | 1 |
| soccer2 | 2 | 5 | 2 | 4 | 4 | 2 |
| soldier | 1 | 1 | 1 | 1 | 1 | 1 |
| sphere | 0 | 0 | 0 | 0 | 0 | 0 |
| tiger | 0 | 0 | 0 | 0 | 0 | 0 |
| traffic | 0 | 0 | 0 | 0 | 0 | 0.2 |
| tunnel | 0 | 0 | 0 | 0 | 0 | 0 |
| wiper | 0.4 | 0 | 0 | 0 | 0 | 0 |
| mean | 0.93 | 1.38 | 1.38 | 1.3 | 1.28 | 1.28 |
| | | | | | | | | 0 |
| | MetaCREST-01 | CREST-Base | CREST-10 | CREST-05 | CREST-03 | CREST-01 |
| --- | --- | --- | --- | --- | --- | --- |
| bag | 0.45 | 0.45 | 0.45 | 0.41 | 0.37 | 0.31 |
| ball1 | 0.8 | 0.8 | 0.8 | 0.81 | 0.8 | 0.8 |
| ball2 | 0.44 | 0.31 | 0.38 | 0.3 | 0.47 | 0.45 |
| basketball | 0.6 | 0.55 | 0.55 | 0.6 | 0.62 | 0.61 |
| birds1 | 0.52 | 0.53 | 0.52 | 0.5 | 0.53 | 0.53 |
| birds2 | 0.29 | 0.28 | 0.32 | 0.28 | 0.28 | 0.28 |
| blanket | 0.58 | 0.61 | 0.61 | 0.64 | 0.62 | 0.61 |
| bmx | 0.24 | 0.42 | 0.42 | 0.42 | 0.43 | 0.42 |
| bolt1 | 0.52 | 0.66 | 0.67 | 0.67 | 0.68 | 0.68 |
| bolt2 | 0.56 | 0.48 | 0.57 | 0.58 | 0.58 | 0.57 |
| book | 0.41 | 0.39 | 0.38 | 0.39 | 0.37 | 0.46 |
| butterfly | 0.45 | 0.4 | 0.4 | 0.41 | 0.4 | 0.41 |
| car1 | 0.74 | 0.74 | 0.75 | 0.76 | 0.75 | 0.74 |
| car2 | 0.77 | 0.73 | 0.74 | 0.75 | 0.77 | 0.78 |
| crossing | 0.71 | 0.69 | 0.69 | 0.7 | 0.71 | 0.71 |
| dinosaur | 0.41 | 0.29 | 0.28 | 0.32 | 0.32 | 0.43 |
| fernando | 0.38 | 0.35 | 0.32 | 0.4 | 0.41 | 0.42 |
| fish1 | 0.43 | 0.4 | 0.42 | 0.53 | 0.52 | 0.52 |
| fish2 | 0.34 | 0.42 | 0.39 | 0.34 | 0.34 | 0.33 |
| fish3 | 0.6 | 0.61 | 0.61 | 0.59 | 0.58 | 0.58 |
| fish4 | 0.46 | 0.43 | 0.31 | 0.42 | 0.43 | 0.43 |
| girl | 0.63 | 0.68 | 0.68 | 0.69 | 0.69 | 0.69 |
| glove | 0.52 | 0.52 | 0.53 | 0.51 | 0.53 | 0.55 |
| godfather | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 |
| graduate | 0.38 | 0.47 | 0.48 | 0.49 | 0.46 | 0.47 |
| gymnastics1 | 0.37 | 0.38 | 0.36 | 0.37 | 0.39 | 0.39 |
| gymnastics2 | 0.5 | 0.5 | 0.49 | 0.52 | 0.5 | 0.5 |
| gymnastics3 | 0.29 | 0.39 | 0.35 | 0.37 | 0.33 | 0.29 |
| gymnastics4 | 0.5 | 0.44 | 0.43 | 0.42 | 0.43 | 0.44 |
| hand | 0.43 | 0.4 | 0.38 | 0.42 | 0.41 | 0.44 |
| handball1 | 0.6 | 0.57 | 0.6 | 0.62 | 0.62 | 0.57 |
| handball2 | 0.46 | 0.49 | 0.52 | 0.52 | 0.54 | 0.44 |
| helicopter | 0.38 | 0.51 | 0.51 | 0.49 | 0.47 | 0.47 |
| iceskater1 | 0.51 | 0.52 | 0.52 | 0.56 | 0.56 | 0.54 |
| iceskater2 | 0.58 | 0.53 | 0.53 | 0.53 | 0.55 | 0.54 |
| leaves | 0.39 | 0.13 | 0.13 | 0.3 | 0.3 | 0.29 |
| marching | 0.74 | 0.75 | 0.75 | 0.74 | 0.74 | 0.75 |
| matrix | 0.66 | 0.55 | 0.55 | 0.61 | 0.61 | 0.54 |
| motocross1 | 0.47 | 0.42 | 0.42 | 0.42 | 0.39 | 0.39 |
| motocross2 | 0.45 | 0.42 | 0.42 | 0.49 | 0.48 | 0.54 |
| nature | 0.46 | 0.3 | 0.3 | 0.32 | 0.29 | 0.32 |
| octopus | 0.4 | 0.32 | 0.33 | 0.44 | 0.43 | 0.37 |
| pedestrian1 | 0.73 | 0.68 | 0.72 | 0.7 | 0.69 | 0.68 | | | pedestrian2 | 0.3 | 0.45 | 0.49 | 0.38 | 0.37 | 0.34 |
| --- | --- | --- | --- | --- | --- | --- |
| rabbit | 0.41 | 0.23 | 0.33 | 0.32 | 0.41 | 0.29 |
| racing | 0.52 | 0.42 | 0.42 | 0.4 | 0.4 | 0.41 |
| road | 0.58 | 0.58 | 0.57 | 0.62 | 0.64 | 0.67 |
| shaking | 0.66 | 0.64 | 0.69 | 0.73 | 0.76 | 0.68 |
| sheep | 0.49 | 0.5 | 0.5 | 0.5 | 0.52 | 0.53 |
| singer1 | 0.6 | 0.65 | 0.68 | 0.61 | 0.57 | 0.41 |
| singer2 | 0.69 | 0.65 | 0.6 | 0.54 | 0.66 | 0.64 |
| singer3 | 0.4 | 0.23 | 0.25 | 0.16 | 0.15 | 0.15 |
| soccer1 | 0.52 | 0.48 | 0.53 | 0.53 | 0.55 | 0.51 |
| soccer2 | 0.56 | 0.6 | 0.57 | 0.57 | 0.57 | 0.57 |
| soldier | 0.39 | 0.37 | 0.36 | 0.36 | 0.37 | 0.4 |
| sphere | 0.49 | 0.46 | 0.44 | 0.41 | 0.39 | 0.41 |
| tiger | 0.71 | 0.68 | 0.68 | 0.67 | 0.68 | 0.66 |
| traffic | 0.79 | 0.79 | 0.8 | 0.8 | 0.76 | 0.73 |
| tunnel | 0.52 | 0.67 | 0.68 | 0.43 | 0.41 | 0.38 |
| wiper | 0.62 | 0.65 | 0.65 | 0.64 | 0.65 | 0.65 |
| mean | 0.51 | 0.5 | 0.51 | 0.51 | 0.51 | 0.5 |
| | | | | | | | | 1 |
| | MetaCREST-01 | CREST-Base | CREST-10 | CREST-05 | CREST-03 | CREST-01 |
| --- | --- | --- | --- | --- | --- | --- |
| bag | 0.45 | 0.45 | 0.45 | 0.41 | 0.37 | 0.31 |
| ball1 | 0.8 | 0.8 | 0.8 | 0.81 | 0.8 | 0.8 |
| ball2 | 0.44 | 0.31 | 0.38 | 0.3 | 0.47 | 0.45 |
| basketball | 0.6 | 0.55 | 0.55 | 0.6 | 0.62 | 0.61 |
| birds1 | 0.52 | 0.53 | 0.52 | 0.5 | 0.53 | 0.53 |
| birds2 | 0.29 | 0.28 | 0.32 | 0.28 | 0.28 | 0.28 |
| blanket | 0.58 | 0.61 | 0.61 | 0.64 | 0.62 | 0.61 |
| bmx | 0.24 | 0.42 | 0.42 | 0.42 | 0.43 | 0.42 |
| bolt1 | 0.52 | 0.66 | 0.67 | 0.67 | 0.68 | 0.68 |
| bolt2 | 0.56 | 0.48 | 0.57 | 0.58 | 0.58 | 0.57 |
| book | 0.41 | 0.39 | 0.38 | 0.39 | 0.37 | 0.46 |
| butterfly | 0.45 | 0.4 | 0.4 | 0.41 | 0.4 | 0.41 |
| car1 | 0.74 | 0.74 | 0.75 | 0.76 | 0.75 | 0.74 |
| car2 | 0.77 | 0.73 | 0.74 | 0.75 | 0.77 | 0.78 |
| crossing | 0.71 | 0.69 | 0.69 | 0.7 | 0.71 | 0.71 |
| dinosaur | 0.41 | 0.29 | 0.28 | 0.32 | 0.32 | 0.43 |
| fernando | 0.38 | 0.35 | 0.32 | 0.4 | 0.41 | 0.42 |
| fish1 | 0.43 | 0.4 | 0.42 | 0.53 | 0.52 | 0.52 |
| fish2 | 0.34 | 0.42 | 0.39 | 0.34 | 0.34 | 0.33 |
| fish3 | 0.6 | 0.61 | 0.61 | 0.59 | 0.58 | 0.58 |
| fish4 | 0.46 | 0.43 | 0.31 | 0.42 | 0.43 | 0.43 |
| girl | 0.63 | 0.68 | 0.68 | 0.69 | 0.69 | 0.69 |
| glove | 0.52 | 0.52 | 0.53 | 0.51 | 0.53 | 0.55 |
| godfather | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 |
| graduate | 0.38 | 0.47 | 0.48 | 0.49 | 0.46 | 0.47 |
| gymnastics1 | 0.37 | 0.38 | 0.36 | 0.37 | 0.39 | 0.39 |
| gymnastics2 | 0.5 | 0.5 | 0.49 | 0.52 | 0.5 | 0.5 |
| gymnastics3 | 0.29 | 0.39 | 0.35 | 0.37 | 0.33 | 0.29 |
| gymnastics4 | 0.5 | 0.44 | 0.43 | 0.42 | 0.43 | 0.44 |
| hand | 0.43 | 0.4 | 0.38 | 0.42 | 0.41 | 0.44 |
| handball1 | 0.6 | 0.57 | 0.6 | 0.62 | 0.62 | 0.57 |
| handball2 | 0.46 | 0.49 | 0.52 | 0.52 | 0.54 | 0.44 |
| helicopter | 0.38 | 0.51 | 0.51 | 0.49 | 0.47 | 0.47 |
| iceskater1 | 0.51 | 0.52 | 0.52 | 0.56 | 0.56 | 0.54 |
| iceskater2 | 0.58 | 0.53 | 0.53 | 0.53 | 0.55 | 0.54 |
| leaves | 0.39 | 0.13 | 0.13 | 0.3 | 0.3 | 0.29 |
| marching | 0.74 | 0.75 | 0.75 | 0.74 | 0.74 | 0.75 |
| matrix | 0.66 | 0.55 | 0.55 | 0.61 | 0.61 | 0.54 |
| motocross1 | 0.47 | 0.42 | 0.42 | 0.42 | 0.39 | 0.39 |
| motocross2 | 0.45 | 0.42 | 0.42 | 0.49 | 0.48 | 0.54 |
| nature | 0.46 | 0.3 | 0.3 | 0.32 | 0.29 | 0.32 |
| octopus | 0.4 | 0.32 | 0.33 | 0.44 | 0.43 | 0.37 |
| pedestrian1 | 0.73 | 0.68 | 0.72 | 0.7 | 0.69 | 0.68 | | | soccer1 | 2.93 | 3 | 2 | 2 | 1 | 1 |
| --- | --- | --- | --- | --- | --- | --- |
| soccer2 | 2 | 5 | 2 | 4 | 4 | 2 |
| soldier | 1 | 1 | 1 | 1 | 1 | 1 |
| sphere | 0 | 0 | 0 | 0 | 0 | 0 |
| tiger | 0 | 0 | 0 | 0 | 0 | 0 |
| traffic | 0 | 0 | 0 | 0 | 0 | 0.2 |
| tunnel | 0 | 0 | 0 | 0 | 0 | 0 |
| wiper | 0.4 | 0 | 0 | 0 | 0 | 0 |
| mean | 0.93 | 1.38 | 1.38 | 1.3 | 1.28 | 1.28 |
| | | | | | | | | 0 |
| Method | r | b | Total |
| --- | --- | --- | --- |
| VoronoiLSH(T=64) | 6 | 15 | 90 | | | CrossPolytope(T=64) | 6 | 18 | 108 |
| --- | --- | --- | --- |
| Hyperplane(T=6) | 5 | 22 | 110 |
| FeatureHashing(T=64,k=1) | 7 | 16 | 112 |
| FastCrossPolytope(T=64) | 6 | 15 | 90 |
| DirectionalFeatureHashing(T=6) | 5 | 20 | 100 | | 1 |
| Method | r | b | Total |
| --- | --- | --- | --- |
| VoronoiLSH(T=64) | 6 | 15 | 90 | | | Methods | mAP |
| --- | --- |
| Fast-RCNN+SRBBS | 55.7 |
| Fast-RCNN+SRBBS+RBB | 69.6 |
| Fast-RCNN+SRBBS+RBB+RRoI | 75.7 |
| RRD(Ours) | 84.3 | | 0 |
| Method | r | b | Total |
| --- | --- | --- | --- |
| VoronoiLSH(T=64) | 6 | 15 | 90 | | | CrossPolytope(T=64) | 6 | 18 | 108 |
| --- | --- | --- | --- |
| Hyperplane(T=6) | 5 | 22 | 110 |
| FeatureHashing(T=64,k=1) | 7 | 16 | 112 |
| FastCrossPolytope(T=64) | 6 | 15 | 90 |
| DirectionalFeatureHashing(T=6) | 5 | 20 | 100 | | 1 |
| Method | r | b | Total |
| --- | --- | --- | --- |
| VoronoiLSH(T=64) | 6 | 15 | 90 | | | Fast-RCNN+SRBBS+RBB+RRoI | 75.7 |
| --- | --- |
| RRD(Ours) | 84.3 | | 0 |
| Ck | thek-thcluster |
| --- | --- |
| x | thepositionincanonicalframeforanodeofclusterCk |
| y | thepositioninliveframetforanodeinclusterCk |
| ck | thecentroidpositionofclusterCincanonicalframek |
| c | thecentroidpositionofclusterCinliveframetk |
| nk | thenumberofnodesbelongingtoclusterCincanonicalframek | | | A(C)k | thecrosscovariancematrixofclusterCk |
| --- | --- |
| (R,t) | rotationandtranslationofclusterCk |
| (R,t) | optimalrotationandtranslationofclusterCk |
| σkq | theq-thsingularvalueofA(C)k | | 1 |
| Ck | thek-thcluster |
| --- | --- |
| x | thepositionincanonicalframeforanodeofclusterCk |
| y | thepositioninliveframetforanodeinclusterCk |
| ck | thecentroidpositionofclusterCincanonicalframek |
| c | thecentroidpositionofclusterCinliveframetk |
| nk | thenumberofnodesbelongingtoclusterCincanonicalframek | | | Abbreviation | Histogramofwhichplane |
| --- | --- |
| LBP-TOP | XY+XT+YT |
| LBP-XYOT | XT+YT |
| LBP-XOT | XT |
| LBP-YOT | YT |
| LBP | XY | | 0 |
| Ck | thek-thcluster |
| --- | --- |
| x | thepositionincanonicalframeforanodeofclusterCk |
| y | thepositioninliveframetforanodeinclusterCk |
| ck | thecentroidpositionofclusterCincanonicalframek |
| c | thecentroidpositionofclusterCinliveframetk |
| nk | thenumberofnodesbelongingtoclusterCincanonicalframek | | | A(C)k | thecrosscovariancematrixofclusterCk |
| --- | --- |
| (R,t) | rotationandtranslationofclusterCk |
| (R,t) | optimalrotationandtranslationofclusterCk |
| σkq | theq-thsingularvalueofA(C)k | | 1 |
| Ck | thek-thcluster |
| --- | --- |
| x | thepositionincanonicalframeforanodeofclusterCk |
| y | thepositioninliveframetforanodeinclusterCk |
| ck | thecentroidpositionofclusterCincanonicalframek |
| c | thecentroidpositionofclusterCinliveframetk |
| nk | thenumberofnodesbelongingtoclusterCincanonicalframek | | | LBP-XOT | XT |
| --- | --- |
| LBP-YOT | YT |
| LBP | XY | | 0 |
| | RFA/K-means | MSA/MRA | Relion | EMAN2 | Xmipp | ASPIRE |
| --- | --- | --- | --- | --- | --- | --- |
| SNR=1/50 | 0.45 | 0.97 | 0.79 | 0.74 | 0.83 | 1.00 |
| SNR=1/100 | 0.09 | 0.87 | 0.70 | 0.45 | 0.68 | 0.99 | | | SNR=1/150 | 0.07 | 0.67 | 0.52 | 0.13 | 0.48 | 0.90 |
| --- | --- | --- | --- | --- | --- | --- |
| Timing(hrs) | 1.5 | 7.5 | 16 | 12 | 42 | 0.5 | | 1 |
| | RFA/K-means | MSA/MRA | Relion | EMAN2 | Xmipp | ASPIRE |
| --- | --- | --- | --- | --- | --- | --- |
| SNR=1/50 | 0.45 | 0.97 | 0.79 | 0.74 | 0.83 | 1.00 |
| SNR=1/100 | 0.09 | 0.87 | 0.70 | 0.45 | 0.68 | 0.99 | | | | noFBsPCAdenoising | FBsPCAdenoising |
| --- | --- | --- |
| RFA/K-means | 0.09 | 0.48 |
| MSA/MRA | 0.87 | 0.95 |
| EMAN2 | 0.45 | 0.76 |
| Xmipp | 0.68 | 0.96 | | 0 |
| | RFA/K-means | MSA/MRA | Relion | EMAN2 | Xmipp | ASPIRE |
| --- | --- | --- | --- | --- | --- | --- |
| SNR=1/50 | 0.45 | 0.97 | 0.79 | 0.74 | 0.83 | 1.00 |
| SNR=1/100 | 0.09 | 0.87 | 0.70 | 0.45 | 0.68 | 0.99 | | | SNR=1/150 | 0.07 | 0.67 | 0.52 | 0.13 | 0.48 | 0.90 |
| --- | --- | --- | --- | --- | --- | --- |
| Timing(hrs) | 1.5 | 7.5 | 16 | 12 | 42 | 0.5 | | 1 |
| | RFA/K-means | MSA/MRA | Relion | EMAN2 | Xmipp | ASPIRE |
| --- | --- | --- | --- | --- | --- | --- |
| SNR=1/50 | 0.45 | 0.97 | 0.79 | 0.74 | 0.83 | 1.00 |
| SNR=1/100 | 0.09 | 0.87 | 0.70 | 0.45 | 0.68 | 0.99 | | | EMAN2 | 0.45 | 0.76 |
| --- | --- | --- |
| Xmipp | 0.68 | 0.96 | | 0 |
| | | 1987-1995 | 2006-2008 |
| --- | --- | --- | --- |
| Books | different | 1984 | 2103 | | | conflict | 2528 | 2776 | |
| --- | --- | --- | --- |
| Newspaper | different | 1146 | 1661 |
| conflict | 2368 | 3092 | | | 1 |
| | | 1987-1995 | 2006-2008 |
| --- | --- | --- | --- |
| Books | different | 1984 | 2103 | | | Papers | Year |
| --- | --- |
| | 2005 |
| | 2006 |
| | 2007 |
| | 2008 |
| | 2010 |
| | 2011 |
| | 2012 |
| | 2013 |
| | 2014 |
| | 2015 |
| | 2016 |
| | 2017 | | 0 |
| | | 1987-1995 | 2006-2008 |
| --- | --- | --- | --- |
| Books | different | 1984 | 2103 | | | conflict | 2528 | 2776 | |
| --- | --- | --- | --- |
| Newspaper | different | 1146 | 1661 |
| conflict | 2368 | 3092 | | | 1 |
| | | 1987-1995 | 2006-2008 |
| --- | --- | --- | --- |
| Books | different | 1984 | 2103 | | | | 2016 |
| --- | --- |
| | 2017 | | 0 |
| 2DConvolutional(filters=64,kernelsize=3,activation=”relu”,padding=”same”) |
| --- |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) | | | MaxPooling(poolsize=(2,2),strides=(2,2)) |
| --- |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| Dense(1024,activation=“relu”) |
| Dropout(0.5) |
| Dense(512,activation=“relu”) |
| Dropout(0.5) |
| Dense(1,activation=None) | | 1 |
| 2DConvolutional(filters=64,kernelsize=3,activation=”relu”,padding=”same”) |
| --- |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) | | | 3DConvolutional(filters=32,kernelsize=5,strides=2,activation=”relu”) |
| --- |
| 3DConvolutional(filters=32,kernelsize=5,strides=2,activation=”relu”) |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| Dense(128,activation=“relu”) |
| Dense(64,activation=“relu”) |
| Dense(1,activation=None) | | 0 |
| 2DConvolutional(filters=64,kernelsize=3,activation=”relu”,padding=”same”) |
| --- |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) | | | 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| --- |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=512,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| Dense(1024,activation=“relu”) |
| Dropout(0.5) |
| Dense(512,activation=“relu”) |
| Dropout(0.5) |
| Dense(1,activation=None) | | 1 |
| 2DConvolutional(filters=64,kernelsize=3,activation=”relu”,padding=”same”) |
| --- |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=64,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=128,kernelsize=3,activation=“relu”,padding=“same”) |
| MaxPooling(poolsize=(2,2),strides=(2,2)) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) |
| 2DConvolutional(filters=256,kernelsize=3,activation=“relu”,padding=“same”) | | | 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| --- |
| 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| Dense(128,activation=“relu”) |
| Dense(64,activation=“relu”) |
| Dense(1,activation=None) | | 0 |
| n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 191520 |
| 10 | 42058800 |
| 11 | 7864256400 |
| 12 | 1407126890400 |
| 13 | 257752421166240 |
| 14 | 50607986220311520 |
| 15 | 10995419195575214400 |
| 16 | 2692773804667509763200 | | | 17 | 747221542837742897724800 |
| --- | --- |
| 18 | 233698171655650029030743040 |
| 19 | 81472765051132560093387934080 |
| 20 | 31268587126068905034073041062400 | | 1 |
| n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 191520 |
| 10 | 42058800 |
| 11 | 7864256400 |
| 12 | 1407126890400 |
| 13 | 257752421166240 |
| 14 | 50607986220311520 |
| 15 | 10995419195575214400 |
| 16 | 2692773804667509763200 | | | n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 5040 |
| 10 | 957600 |
| 11 | 123354000 |
| 12 | 16842764400 |
| 13 | 2764379217600 |
| 14 | 527554510282800 |
| 15 | 114387072405606000 |
| 16 | 27728561968887780000 |
| 17 | 7418031804967840056000 |
| 18 | 2167306256125914230527200 |
| 19 | 685709965521372865035362400 |
| 20 | 233306923207078035272369412000 | | 0 |
| n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 191520 |
| 10 | 42058800 |
| 11 | 7864256400 |
| 12 | 1407126890400 |
| 13 | 257752421166240 |
| 14 | 50607986220311520 |
| 15 | 10995419195575214400 | | | 16 | 2692773804667509763200 |
| --- | --- |
| 17 | 747221542837742897724800 |
| 18 | 233698171655650029030743040 |
| 19 | 81472765051132560093387934080 |
| 20 | 31268587126068905034073041062400 | | 1 |
| n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 191520 |
| 10 | 42058800 |
| 11 | 7864256400 |
| 12 | 1407126890400 |
| 13 | 257752421166240 |
| 14 | 50607986220311520 |
| 15 | 10995419195575214400 | | | 14 | 527554510282800 |
| --- | --- |
| 15 | 114387072405606000 |
| 16 | 27728561968887780000 |
| 17 | 7418031804967840056000 |
| 18 | 2167306256125914230527200 |
| 19 | 685709965521372865035362400 |
| 20 | 233306923207078035272369412000 | | 0 |
| Method | NumberofPaths | RunningTime(ms) |
| --- | --- | --- |
| ILP | 2.0069 | 7.2133 |
| RSG | 2.0160 | 2.0167 | | | RR | 2.0482 | 0.0272 |
| --- | --- | --- |
| MSPG | 2.2241 | 0.1911 |
| EPS | 2.551 | 1.6000 | | 1 |
| Method | NumberofPaths | RunningTime(ms) |
| --- | --- | --- |
| ILP | 2.0069 | 7.2133 |
| RSG | 2.0160 | 2.0167 | | | method | AU01 | AU02 | AU04 | AU06 | AU09 | AU12 | AU25 | AU26 | avg |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| APL | 11.4 | 12.0 | 30.1 | 12.4 | 10.1 | 65.9 | 21.4 | 26.0 | 23.8 |
| DRML | 17.3 | 17.7 | 37.4 | 29.0 | 10.7 | 37.7 | 38.5 | 20.1 | 26.7 |
| ROI | 41.5 | 26.4 | 66.4 | 50.7 | 8.5 | 89.3 | 88.9 | 15.6 | 48.5 |
| DSIN | [42.4] | [39.0] | [68.4] | 28.6 | [46.8] | 70.8 | [90.4] | [42.2] | [53.6] |
| DSIN | 46.9 | 42.5 | 68.8 | [32.0] | 51.8 | [73.1] | 91.9 | 46.6 | 56.7 | | 0 |
| Method | NumberofPaths | RunningTime(ms) |
| --- | --- | --- |
| ILP | 2.0069 | 7.2133 | | | RSG | 2.0160 | 2.0167 |
| --- | --- | --- |
| RR | 2.0482 | 0.0272 |
| MSPG | 2.2241 | 0.1911 |
| EPS | 2.551 | 1.6000 | | 1 |
| Method | NumberofPaths | RunningTime(ms) |
| --- | --- | --- |
| ILP | 2.0069 | 7.2133 | | | DRML | 17.3 | 17.7 | 37.4 | 29.0 | 10.7 | 37.7 | 38.5 | 20.1 | 26.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ROI | 41.5 | 26.4 | 66.4 | 50.7 | 8.5 | 89.3 | 88.9 | 15.6 | 48.5 |
| DSIN | [42.4] | [39.0] | [68.4] | 28.6 | [46.8] | 70.8 | [90.4] | [42.2] | [53.6] |
| DSIN | 46.9 | 42.5 | 68.8 | [32.0] | 51.8 | [73.1] | 91.9 | 46.6 | 56.7 | | 0 |
| FeatureType | datasetI | datasetII |
| --- | --- | --- |
| Extension | .url | .xls |
| Extension | .properties | .txt |
| Extension | .mdm | .html |
| Extension | .pas | .net |
| FileSize | - | - |
| BytesUsed | - | - | | | Lastaccesstimediff | - | - |
| --- | --- | --- |
| Changetimediff | - | - |
| Lastmodifiedtimediff | - | - |
| Text | “feature” | “username” | | 1 |
| FeatureType | datasetI | datasetII |
| --- | --- | --- |
| Extension | .url | .xls |
| Extension | .properties | .txt |
| Extension | .mdm | .html |
| Extension | .pas | .net |
| FileSize | - | - |
| BytesUsed | - | - | | | Featurecategory | datasetI | datasetII |
| --- | --- | --- |
| Filename | 29736 | 14861 |
| Filepath | 788 | 315 |
| Fileextensions | 1170 | 316 |
| Filesizerelated | 2 | 2 |
| Timerelated | 3 | 3 |
| Totalfeaturesize | 31699 | 15497 | | 0 |
| FeatureType | datasetI | datasetII |
| --- | --- | --- |
| Extension | .url | .xls |
| Extension | .properties | .txt |
| Extension | .mdm | .html |
| Extension | .pas | .net |
| FileSize | - | - |
| BytesUsed | - | - |
| Lastaccesstimediff | - | - | | | Changetimediff | - | - |
| --- | --- | --- |
| Lastmodifiedtimediff | - | - |
| Text | “feature” | “username” | | 1 |
| FeatureType | datasetI | datasetII |
| --- | --- | --- |
| Extension | .url | .xls |
| Extension | .properties | .txt |
| Extension | .mdm | .html |
| Extension | .pas | .net |
| FileSize | - | - |
| BytesUsed | - | - |
| Lastaccesstimediff | - | - | | | Fileextensions | 1170 | 316 |
| --- | --- | --- |
| Filesizerelated | 2 | 2 |
| Timerelated | 3 | 3 |
| Totalfeaturesize | 31699 | 15497 | | 0 |
| Input | Algorithm | Precision | Recall | F1 |
| --- | --- | --- | --- | --- |
| Rawfeaturevector | OCNN | 0.8029±0.1497 | 0.9075±0.0596 | 0.8383±0.0660 |
| OCGP | 0.9700±0.0222 | 0.7308±0.0812 | 0.8302±0.0450 | |
| OCSVM | 0.6590±0.0100 | 0.9404±0.0017 | 0.7749±0.0068 | | | | OCAN | 0.9755±0.0110 | 0.7416±0.0498 | 0.8416±0.0330 | |
| --- | --- | --- | --- | --- |
| Transactionrepresentation | OCNN | 0.7058±0.1396 | 0.9390±0.0786 | 0.7910±0.0608 |
| OCGP | 0.8813±0.1177 | 0.8566±0.0822 | 0.8576±0.0417 | |
| OCSVM | 0.6547±0.0151 | 0.9509±0.0101 | 0.7755±0.0127 | |
| OCAN | 0.9067±0.0614 | 0.8320±0.0319 | 0.8656±0.0220 | | | 1 |
| Input | Algorithm | Precision | Recall | F1 |
| --- | --- | --- | --- | --- |
| Rawfeaturevector | OCNN | 0.8029±0.1497 | 0.9075±0.0596 | 0.8383±0.0660 |
| OCGP | 0.9700±0.0222 | 0.7308±0.0812 | 0.8302±0.0450 | |
| OCSVM | 0.6590±0.0100 | 0.9404±0.0017 | 0.7749±0.0068 | | | | Input | Algorithm | Precision | Recall | F1 |
| --- | --- | --- | --- | --- |
| Rawfeaturevector | OCNN | 0.5680±0.0129 | 0.8646±0.0599 | 0.6845±0.0184 |
| OCGP | 0.5767±0.0087 | 0.9000±0.0560 | 0.7023±0.0193 | |
| OCSVM | 0.6631±0.0057 | 0.9829±0.0011 | 0.7919±0.0040 | |
| Userrepresentation | OCNN | 0.8314±0.0351 | 0.8028±0.0476 | 0.8150±0.0163 |
| OCGP | 0.8381±,0.0225 | 0.8289±0.0374 | 0.8326±0.0158 | |
| OCSVM | 0.6558±0.0058 | 0.9590±0.0096 | 0.7789±0.0064 | |
| OCAN | 0.9067±0.0615 | 0.9292±0.0348 | 0.9010±0.0228 | |
| Userrepresentation | OCAN-r | 0.8673±0.0355 | 0.8759±0.0529 | 0.8701±0.0267 | | 0 |
| Input | Algorithm | Precision | Recall | F1 |
| --- | --- | --- | --- | --- |
| Rawfeaturevector | OCNN | 0.8029±0.1497 | 0.9075±0.0596 | 0.8383±0.0660 | | | OCGP | 0.9700±0.0222 | 0.7308±0.0812 | 0.8302±0.0450 | |
| --- | --- | --- | --- | --- |
| OCSVM | 0.6590±0.0100 | 0.9404±0.0017 | 0.7749±0.0068 | |
| OCAN | 0.9755±0.0110 | 0.7416±0.0498 | 0.8416±0.0330 | |
| Transactionrepresentation | OCNN | 0.7058±0.1396 | 0.9390±0.0786 | 0.7910±0.0608 |
| OCGP | 0.8813±0.1177 | 0.8566±0.0822 | 0.8576±0.0417 | |
| OCSVM | 0.6547±0.0151 | 0.9509±0.0101 | 0.7755±0.0127 | |
| OCAN | 0.9067±0.0614 | 0.8320±0.0319 | 0.8656±0.0220 | | | 1 |
| Input | Algorithm | Precision | Recall | F1 |
| --- | --- | --- | --- | --- |
| Rawfeaturevector | OCNN | 0.8029±0.1497 | 0.9075±0.0596 | 0.8383±0.0660 | | | Userrepresentation | OCNN | 0.8314±0.0351 | 0.8028±0.0476 | 0.8150±0.0163 |
| --- | --- | --- | --- | --- |
| OCGP | 0.8381±,0.0225 | 0.8289±0.0374 | 0.8326±0.0158 | |
| OCSVM | 0.6558±0.0058 | 0.9590±0.0096 | 0.7789±0.0064 | |
| OCAN | 0.9067±0.0615 | 0.9292±0.0348 | 0.9010±0.0228 | |
| Userrepresentation | OCAN-r | 0.8673±0.0355 | 0.8759±0.0529 | 0.8701±0.0267 | | 0 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0006±0.0000 | 184.2852 | 188 | - |
| GKKM | 0.0064±0.0000 | 17.0144 | 13 | 0.7982 | | | M=5,µ=0.2500,σ=0.09160 | | | | |
| --- | --- | --- | --- | --- |
| CCBS<br>GCBS | 0.0155±0.0002<br>0.0129±0.0001 | 13.5109<br>15.9691 | 9<br>9 | 0.2454<br>0.2454 |
| M=30,µ=0.0345,σ=0.01740 | | | | |
| CCBS<br>GCBS | 0.0101±0.0001<br>0.0102±0.0001 | 26.1530<br>26.1337 | 36<br>35 | 0.0336<br>0.0341 |
| M=50,µ=0.0204,σ=0.01130 | | | | |
| CCBS<br>GCBS | 0.0099±0.0001<br>0.0092±0.0001 | 38.1907<br>19.4064 | 49<br>49 | 0.0199<br>0.0202 |
| M=70,µ=0.0145,σ=0.00870 | | | | |
| CCBS<br>GCBS | 0.0089±0.0001<br>0.0089±0.0001 | 24.5304<br>26.5687 | 61<br>61 | 0.0141<br>0.0130 |
| M=120,µ=0.0084,σ=0.00590 | | | | |
| CCBS<br>GCBS | 0.0079±0.0000<br>0.0077±0.0000 | 42.4282<br>62.1598 | 84<br>84 | 0.0084<br>0.0084 |
| M=150,µ=0.0067,σ=0.00510 | | | | |
| CCBS<br>GCBS | 0.0074±0.0000<br>0.0076±0.0000 | 55.4628<br>56.0754 | 93<br>92 | 0.0067<br>0.0067 |
| M=188,µ=0.0053,σ=0.00440 | | | | |
| CCBS<br>GCBS | 0.0076±0.0000<br>0.0075±0.0000 | 54.0429<br>56.0656 | 98<br>97 | 0.0043<br>0.0047 |
| M=500,µ=0.0020,σ=0.00260 | | | | |
| CCBS<br>GCBS | 0.0067±0.0000<br>0.0068±0.0000 | 95.6673<br>93.1751 | 122<br>122 | 0.0017<br>0.0016 |
| M=1000,µ=0.0010,σ=0.00190 | | | | |
| CCBS<br>GCBS | 0.0067±0.0000<br>0.0067±0.0000 | 87.0426<br>96.7277 | 131<br>131 | 0.0009<br>0.0009 |
| M=2000,µ=0.0005,σ=0.00150 | | | | |
| CCBS<br>GCBS | 0.0063±0.0000<br>0.0063±0.0000 | 107.6217<br>98.2356 | 137<br>137 | 0.0004<br>0.0004 | | 1 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0006±0.0000 | 184.2852 | 188 | - |
| GKKM | 0.0064±0.0000 | 17.0144 | 13 | 0.7982 | | | Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.1320±0.0161 | 0.3034 | 211 | - |
| GKKM | 0.1325±0.0150 | 1.8930 | 15 | 0.7694 |
| M=5,µ=0.2500,σ=0.10840 | | | | |
| CCBS<br>GCBS | 0.1358±0.0154<br>0.1585±0.0229 | 0.0550<br>0.0227 | 7<br>5 | 0.2441<br>0.2212 |
| M=10,µ=0.1111,σ=0.05890 | | | | |
| CCBS<br>GCBS | 0.1224±0.0125<br>0.1323±0.0158 | 0.0740<br>0.0172 | 14<br>12 | 0.1110<br>0.1076 |
| M=20,µ=0.0526,σ=0.03410 | | | | |
| CCBS<br>GCBS | 0.1132±0.0118<br>0.1179±0.0131 | 0.0731<br>0.0204 | 22<br>20 | 0.0506<br>0.0491 |
| M=30,µ=0.0345,σ=0.02420 | | | | |
| CCBS<br>GCBS | 0.1123±0.0131<br>0.1166±0.0143 | 0.1679<br>0.0248 | 29<br>28 | 0.0339<br>0.0323 | | 0 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0006±0.0000 | 184.2852 | 188 | - |
| GKKM | 0.0064±0.0000 | 17.0144 | 13 | 0.7982 |
| M=5,µ=0.2500,σ=0.09160 | | | | |
| CCBS<br>GCBS | 0.0155±0.0002<br>0.0129±0.0001 | 13.5109<br>15.9691 | 9<br>9 | 0.2454<br>0.2454 |
| M=30,µ=0.0345,σ=0.01740 | | | | | | | CCBS<br>GCBS | 0.0101±0.0001<br>0.0102±0.0001 | 26.1530<br>26.1337 | 36<br>35 | 0.0336<br>0.0341 |
| --- | --- | --- | --- | --- |
| M=50,µ=0.0204,σ=0.01130 | | | | |
| CCBS<br>GCBS | 0.0099±0.0001<br>0.0092±0.0001 | 38.1907<br>19.4064 | 49<br>49 | 0.0199<br>0.0202 |
| M=70,µ=0.0145,σ=0.00870 | | | | |
| CCBS<br>GCBS | 0.0089±0.0001<br>0.0089±0.0001 | 24.5304<br>26.5687 | 61<br>61 | 0.0141<br>0.0130 |
| M=120,µ=0.0084,σ=0.00590 | | | | |
| CCBS<br>GCBS | 0.0079±0.0000<br>0.0077±0.0000 | 42.4282<br>62.1598 | 84<br>84 | 0.0084<br>0.0084 |
| M=150,µ=0.0067,σ=0.00510 | | | | |
| CCBS<br>GCBS | 0.0074±0.0000<br>0.0076±0.0000 | 55.4628<br>56.0754 | 93<br>92 | 0.0067<br>0.0067 |
| M=188,µ=0.0053,σ=0.00440 | | | | |
| CCBS<br>GCBS | 0.0076±0.0000<br>0.0075±0.0000 | 54.0429<br>56.0656 | 98<br>97 | 0.0043<br>0.0047 |
| M=500,µ=0.0020,σ=0.00260 | | | | |
| CCBS<br>GCBS | 0.0067±0.0000<br>0.0068±0.0000 | 95.6673<br>93.1751 | 122<br>122 | 0.0017<br>0.0016 |
| M=1000,µ=0.0010,σ=0.00190 | | | | |
| CCBS<br>GCBS | 0.0067±0.0000<br>0.0067±0.0000 | 87.0426<br>96.7277 | 131<br>131 | 0.0009<br>0.0009 |
| M=2000,µ=0.0005,σ=0.00150 | | | | |
| CCBS<br>GCBS | 0.0063±0.0000<br>0.0063±0.0000 | 107.6217<br>98.2356 | 137<br>137 | 0.0004<br>0.0004 | | 1 |
| Strategy | RMSE±STD | Time | Nb | µ |
| --- | --- | --- | --- | --- |
| SK-Hype | 0.0006±0.0000 | 184.2852 | 188 | - |
| GKKM | 0.0064±0.0000 | 17.0144 | 13 | 0.7982 |
| M=5,µ=0.2500,σ=0.09160 | | | | |
| CCBS<br>GCBS | 0.0155±0.0002<br>0.0129±0.0001 | 13.5109<br>15.9691 | 9<br>9 | 0.2454<br>0.2454 |
| M=30,µ=0.0345,σ=0.01740 | | | | | | | M=30,µ=0.0345,σ=0.02420 | | | | |
| --- | --- | --- | --- | --- |
| CCBS<br>GCBS | 0.1123±0.0131<br>0.1166±0.0143 | 0.1679<br>0.0248 | 29<br>28 | 0.0339<br>0.0323 | | 0 |
| semantic | symbol | defaultvalue |
| --- | --- | --- |
| numberofnodes | n | 300sensors |
| communicationradius | dtrx | 100meters |
| sensingradius | ddtx | 25meters | | | targetspeed | detectionradius | 6km/h |
| --- | --- | --- |
| messagepropagationfrequency | freq | 1MSG/second |
| networkdensity | dens | 10/(100·3.14)*Mag/second | | 1 |
| semantic | symbol | defaultvalue |
| --- | --- | --- |
| numberofnodes | n | 300sensors |
| communicationradius | dtrx | 100meters |
| sensingradius | ddtx | 25meters | | | Parameters | Value | Description |
| --- | --- | --- |
| N | 1000 | Numberofvideocontents |
| Ld | 30m | D2Ddistancethreshold |
| Cd | 80 | CachesizeofD2Dtransmitusers |
| Cf | 100∼500 | CachesizeofF-APs |
| Bd | 300MHz | BandwidthofD2Dusers |
| Bf | 100MHz | BandwidthofF-APs |
| Pd | 13dBm | TransmitpowerofD2Dtransmituser |
| Pf | 23dBm | TransmitpowerofF-AP |
| λru | −5<br>6×10 | Intensityofcontentrequireusers |
| λtu | −5<br>5×10 | IntensityofD2Dtransmitusers |
| λf | −5<br>3×10 | IntensityofF-APs |
| λg | −6<br>2×10 | Intensityofgateways |
| α | 4 | Pathlossexponent | | 0 |
| semantic | symbol | defaultvalue |
| --- | --- | --- |
| numberofnodes | n | 300sensors |
| communicationradius | dtrx | 100meters |
| sensingradius | ddtx | 25meters | | | targetspeed | detectionradius | 6km/h |
| --- | --- | --- |
| messagepropagationfrequency | freq | 1MSG/second |
| networkdensity | dens | 10/(100·3.14)*Mag/second | | 1 |
| semantic | symbol | defaultvalue |
| --- | --- | --- |
| numberofnodes | n | 300sensors |
| communicationradius | dtrx | 100meters |
| sensingradius | ddtx | 25meters | | | Pf | 23dBm | TransmitpowerofF-AP |
| --- | --- | --- |
| λru | −5<br>6×10 | Intensityofcontentrequireusers |
| λtu | −5<br>5×10 | IntensityofD2Dtransmitusers |
| λf | −5<br>3×10 | IntensityofF-APs |
| λg | −6<br>2×10 | Intensityofgateways |
| α | 4 | Pathlossexponent | | 0 |
| LM | dev | eval | | |
| --- | --- | --- | --- | --- |
| Vit | CN | Vit | CN | |
| ng4 | 30.4 | 29.8 | 31.0 | 30.7 | | | +uni-RNN | 28.5 | 27.8 | 28.7 | 28.4 |
| --- | --- | --- | --- | --- |
| +succ-RNN(1word)<br>+succ-RNN(3word) | 28.0<br>27.8 | 27.5<br>27.4 | 28.6<br>28.5 | 28.1<br>28.0 | | 1 |
| LM | dev | eval | | |
| --- | --- | --- | --- | --- |
| Vit | CN | Vit | CN | |
| ng4 | 30.4 | 29.8 | 31.0 | 30.7 | | | LM | #succ<br>words | dev | eval | | |
| --- | --- | --- | --- | --- | --- |
| Vit | CN | Vit | CN | | |
| ng4 | - | 24.46 | 24.20 | 24.68 | 24.44 |
| +uni-rnn | - | 22.49 | 22.33 | 22.53 | 22.30 |
| +su-rnn | 1<br>3 | 22.32<br>21.97? | 22.13<br>21.77 | 22.20<br>21.70? | 22.09<br>21.53 | | 0 |
| LM | dev | eval | | |
| --- | --- | --- | --- | --- |
| Vit | CN | Vit | CN | |
| ng4 | 30.4 | 29.8 | 31.0 | 30.7 | | | +uni-RNN | 28.5 | 27.8 | 28.7 | 28.4 |
| --- | --- | --- | --- | --- |
| +succ-RNN(1word)<br>+succ-RNN(3word) | 28.0<br>27.8 | 27.5<br>27.4 | 28.6<br>28.5 | 28.1<br>28.0 | | 1 |
| LM | dev | eval | | |
| --- | --- | --- | --- | --- |
| Vit | CN | Vit | CN | |
| ng4 | 30.4 | 29.8 | 31.0 | 30.7 | | | +uni-rnn | - | 22.49 | 22.33 | 22.53 | 22.30 |
| --- | --- | --- | --- | --- | --- |
| +su-rnn | 1<br>3 | 22.32<br>21.97? | 22.13<br>21.77 | 22.20<br>21.70? | 22.09<br>21.53 | | 0 |
| Dataset | AveragePoolMemory(inKBs) | | |
| --- | --- | --- | --- |
| | FCT | EPa | EP |
| Flight | 32.1 | 20.2 | 18.1 |
| Electricity | 31.6 | 16.1 | 14.1 | | | Rot.Hyperplane | 48.4 | 38.6 | 27.9 |
| --- | --- | --- | --- |
| Spam | 17.3 | 17.2 | 16.4 | | 1 |
| Dataset | AveragePoolMemory(inKBs) | | |
| --- | --- | --- | --- |
| | FCT | EPa | EP |
| Flight | 32.1 | 20.2 | 18.1 |
| Electricity | 31.6 | 16.1 | 14.1 | | | Dataset | FCT | EPa | EP |
| --- | --- | --- | --- |
| Flight | 797.2 | 731.2 | 836.9 |
| Electricity | 11600.3 | 9002.5 | 11402.5 |
| RotatingHyperplane | 5647.8 | 5413.8 | 5804.5 |
| Spam | 4.2 | 3.9 | 4.2 | | 0 |
| Dataset | AveragePoolMemory(inKBs) | | |
| --- | --- | --- | --- |
| | FCT | EPa | EP |
| Flight | 32.1 | 20.2 | 18.1 | | | Electricity | 31.6 | 16.1 | 14.1 |
| --- | --- | --- | --- |
| Rot.Hyperplane | 48.4 | 38.6 | 27.9 |
| Spam | 17.3 | 17.2 | 16.4 | | 1 |
| Dataset | AveragePoolMemory(inKBs) | | |
| --- | --- | --- | --- |
| | FCT | EPa | EP |
| Flight | 32.1 | 20.2 | 18.1 | | | RotatingHyperplane | 5647.8 | 5413.8 | 5804.5 |
| --- | --- | --- | --- |
| Spam | 4.2 | 3.9 | 4.2 | | 0 |
| 79.75(79.47,80.03) | 80.20(80.02,80.35) | 80.68(80.14,81.21) | 80.99(80.65,81.30) |
| --- | --- | --- | --- |
| 44.98(44.06,45.68) | 46.10(45.37,46.84) | 46.75(46.35,47.36) | 47.02(46.59,47.59) |
| 83.69(83.46,84.07) | 84.63(84.44,84.88) | 85.18(84.64,85.59) | 85.38(85.31,85.49) | | | 92.60(92.28,92.76) | 92.87(92.69,93.17) | 93.06(92.81,93.19) | 93.13(92.79,93.32) |
| --- | --- | --- | --- |
| 90.29(89.93,90.61) | 91.42(91.16,91.71) | 91.52(91.23,91.72) | 91.47(91.15,91.64) |
| 81.72(81.21,82.20) | 82.71(82.06,83.30) | 83.44(83.06,83.90) | 83.70(83.31,84.25) |
| 89.15(88.83,89.47) | 89.39(89.14,89.56) | 89.30(89.16,89.60) | 89.37(88.99,89.61) | | 1 |
| 79.75(79.47,80.03) | 80.20(80.02,80.35) | 80.68(80.14,81.21) | 80.99(80.65,81.30) |
| --- | --- | --- | --- |
| 44.98(44.06,45.68) | 46.10(45.37,46.84) | 46.75(46.35,47.36) | 47.02(46.59,47.59) |
| 83.69(83.46,84.07) | 84.63(84.44,84.88) | 85.18(84.64,85.59) | 85.38(85.31,85.49) | | | 79.22(79.02,79.57) | 45.46(44.88,45.96) | 83.24(82.93,83.67) | 91.97(91.64,92.17) | 85.86(85.54,86.13) | 80.24(79.64,80.62) |
| --- | --- | --- | --- | --- | --- |
| 80.27(79.94,80.51) | 46.18(45.74,46.52) | 84.37(83.96,94.70) | 92.83(92.58,93.06) | 90.33(90.05,90.62) | 80.71(79.72,81.37) |
| 80.35(80.05,80.65) | 46.18(45.69,46.63) | 84.38(84.04,84.61) | 92.54(92.44,92.68) | 90.06(89.84,90.26) | 81.11(80.54,81.55) |
| 80.25(79.89,80.60) | 45.96(45.44,46.55) | 84.24(83.40,84.59) | 92.50(92.33,92.68) | 89.44(89.07,89.84) | 81.53(81.09,82.05) |
| 80.02(79.68,80.17) | 45.65(45.08,46.09) | 83.90(83.40,84.37) | 92.31(92.19,92.50) | 88.81(88.53,89.03) | 81.19(80.89,81.61) |
| 79.59(79.36,79.75) | 45.19(44.67,45.62) | 83.64(83.32,83.95) | 92.02(91.86,92.23) | 88.41(87.96,88.71) | 81.36(80.72,82.04) |
| 79.33(78.76,79.75) | 45.02(44.15,45.77) | 83.30(83.03,83.60) | 91.87(91.70,91.99) | 88.46(88.21,88.85) | 81.42(81.03,81.90) |
| 79.05(78.91,79.21) | 44.61(44.05,45.53) | 83.24(82.82,83.70) | 91.95(91.59,92.16) | 88.23(87.57,88.56) | 81.16(80.69,81.57) |
| 79.04(78.86,79.30) | 44.66(44.42,44.91) | 83.09(82.61,83.42) | 91.85(91.74,92.00) | 88.41(87.98,88.67) | 81.28(80.96,81.55) | | 0 |
| 79.75(79.47,80.03) | 80.20(80.02,80.35) | 80.68(80.14,81.21) | 80.99(80.65,81.30) |
| --- | --- | --- | --- |
| 44.98(44.06,45.68) | 46.10(45.37,46.84) | 46.75(46.35,47.36) | 47.02(46.59,47.59) |
| 83.69(83.46,84.07) | 84.63(84.44,84.88) | 85.18(84.64,85.59) | 85.38(85.31,85.49) |
| 92.60(92.28,92.76) | 92.87(92.69,93.17) | 93.06(92.81,93.19) | 93.13(92.79,93.32) | | | 90.29(89.93,90.61) | 91.42(91.16,91.71) | 91.52(91.23,91.72) | 91.47(91.15,91.64) |
| --- | --- | --- | --- |
| 81.72(81.21,82.20) | 82.71(82.06,83.30) | 83.44(83.06,83.90) | 83.70(83.31,84.25) |
| 89.15(88.83,89.47) | 89.39(89.14,89.56) | 89.30(89.16,89.60) | 89.37(88.99,89.61) | | 1 |
| 79.75(79.47,80.03) | 80.20(80.02,80.35) | 80.68(80.14,81.21) | 80.99(80.65,81.30) |
| --- | --- | --- | --- |
| 44.98(44.06,45.68) | 46.10(45.37,46.84) | 46.75(46.35,47.36) | 47.02(46.59,47.59) |
| 83.69(83.46,84.07) | 84.63(84.44,84.88) | 85.18(84.64,85.59) | 85.38(85.31,85.49) |
| 92.60(92.28,92.76) | 92.87(92.69,93.17) | 93.06(92.81,93.19) | 93.13(92.79,93.32) | | | 79.05(78.91,79.21) | 44.61(44.05,45.53) | 83.24(82.82,83.70) | 91.95(91.59,92.16) | 88.23(87.57,88.56) | 81.16(80.69,81.57) |
| --- | --- | --- | --- | --- | --- |
| 79.04(78.86,79.30) | 44.66(44.42,44.91) | 83.09(82.61,83.42) | 91.85(91.74,92.00) | 88.41(87.98,88.67) | 81.28(80.96,81.55) | | 0 |
| | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 |
| 8401 | 26 | 5 | 5 | 0.839 | 0.839 | 0.839 |
| 10558a | 122 | 1 | 8 | 0.992 | 0.938 | 0.964 |
| 23585a | 149 | 10 | 16 | 0.937 | 0.903 | 0.92 |
| 23585b | 103 | 3 | 1 | 0.972 | 0.99 | 0.981 |
| 34921a | 70 | 4 | 5 | 0.946 | 0.933 | 0.94 |
| 34921b | 91 | 10 | 8 | 0.901 | 0.919 | 0.91 |
| 36553 | 200 | 21 | 14 | 0.905 | 0.935 | 0.92 |
| 50009 | 44 | 28 | 14 | 0.611 | 0.759 | 0.677 |
| 50028 | 81 | 17 | 12 | 0.827 | 0.871 | 0.848 |
| UGS01 | 164 | 8 | 12 | 0.953 | 0.932 | 0.943 |
| UGS04 | 218 | 25 | 5 | 0.897 | 0.978 | 0.936 | | | UGS05 | 21 | 6 | 9 | 0.778 | 0.7 | 0.737 |
| --- | --- | --- | --- | --- | --- | --- |
| UGS09 | 169 | 12 | 24 | 0.934 | 0.876 | 0.904 |
| Total | 1756 | 285 | 244 | 0.86 | 0.878 | 0.869 | | 1 |
| | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 |
| 8401 | 26 | 5 | 5 | 0.839 | 0.839 | 0.839 |
| 10558a | 122 | 1 | 8 | 0.992 | 0.938 | 0.964 |
| 23585a | 149 | 10 | 16 | 0.937 | 0.903 | 0.92 |
| 23585b | 103 | 3 | 1 | 0.972 | 0.99 | 0.981 |
| 34921a | 70 | 4 | 5 | 0.946 | 0.933 | 0.94 |
| 34921b | 91 | 10 | 8 | 0.901 | 0.919 | 0.91 |
| 36553 | 200 | 21 | 14 | 0.905 | 0.935 | 0.92 |
| 50009 | 44 | 28 | 14 | 0.611 | 0.759 | 0.677 |
| 50028 | 81 | 17 | 12 | 0.827 | 0.871 | 0.848 |
| UGS01 | 164 | 8 | 12 | 0.953 | 0.932 | 0.943 |
| UGS04 | 218 | 25 | 5 | 0.897 | 0.978 | 0.936 | | | | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 39 | 96 | 82 | 0.289 | 0.322 | 0.305 |
| 8024 | 96 | 29 | 10 | 0.768 | 0.906 | 0.831 |
| 8386 | 114 | 5 | 4 | 0.958 | 0.966 | 0.962 |
| 8401 | 30 | 8 | 1 | 0.789 | 0.968 | 0.87 |
| 10558a | 125 | 1 | 5 | 0.992 | 0.962 | 0.977 |
| 23585a | 159 | 8 | 6 | 0.952 | 0.964 | 0.958 |
| 23585b | 103 | 4 | 1 | 0.963 | 0.99 | 0.976 |
| 34921a | 71 | 6 | 4 | 0.922 | 0.947 | 0.934 |
| 34921b | 91 | 11 | 8 | 0.892 | 0.919 | 0.905 |
| 36553 | 202 | 26 | 12 | 0.886 | 0.944 | 0.914 |
| 50009 | 53 | 29 | 5 | 0.646 | 0.914 | 0.757 |
| 50028 | 89 | 18 | 4 | 0.832 | 0.957 | 0.89 |
| UGS01 | 171 | 12 | 5 | 0.934 | 0.972 | 0.953 |
| UGS04 | 222 | 15 | 1 | 0.937 | 0.996 | 0.965 |
| UGS05 | 26 | 21 | 4 | 0.553 | 0.867 | 0.675 |
| UGS09 | 176 | 17 | 17 | 0.912 | 0.912 | 0.912 |
| Total | 1827 | 313 | 173 | 0.854 | 0.913 | 0.883 | | 0 |
| | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 | | | 8401 | 26 | 5 | 5 | 0.839 | 0.839 | 0.839 |
| --- | --- | --- | --- | --- | --- | --- |
| 10558a | 122 | 1 | 8 | 0.992 | 0.938 | 0.964 |
| 23585a | 149 | 10 | 16 | 0.937 | 0.903 | 0.92 |
| 23585b | 103 | 3 | 1 | 0.972 | 0.99 | 0.981 |
| 34921a | 70 | 4 | 5 | 0.946 | 0.933 | 0.94 |
| 34921b | 91 | 10 | 8 | 0.901 | 0.919 | 0.91 |
| 36553 | 200 | 21 | 14 | 0.905 | 0.935 | 0.92 |
| 50009 | 44 | 28 | 14 | 0.611 | 0.759 | 0.677 |
| 50028 | 81 | 17 | 12 | 0.827 | 0.871 | 0.848 |
| UGS01 | 164 | 8 | 12 | 0.953 | 0.932 | 0.943 |
| UGS04 | 218 | 25 | 5 | 0.897 | 0.978 | 0.936 |
| UGS05 | 21 | 6 | 9 | 0.778 | 0.7 | 0.737 |
| UGS09 | 169 | 12 | 24 | 0.934 | 0.876 | 0.904 |
| Total | 1756 | 285 | 244 | 0.86 | 0.878 | 0.869 | | 1 |
| | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 | | | UGS04 | 222 | 15 | 1 | 0.937 | 0.996 | 0.965 |
| --- | --- | --- | --- | --- | --- | --- |
| UGS05 | 26 | 21 | 4 | 0.553 | 0.867 | 0.675 |
| UGS09 | 176 | 17 | 17 | 0.912 | 0.912 | 0.912 |
| Total | 1827 | 313 | 173 | 0.854 | 0.913 | 0.883 | | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.