RANK | SUBMISSION_ID | EVALUATION_NAME | TRACK_NAME | TEAM_NAME | SYSTEM_NAME | METRIC_NAME | METRIC_VALUE |
---|---|---|---|---|---|---|---|
1 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00069_Team-Vision_20181009-155732-6403.sr-20181009-155732-6965 | ActEV-2018 | AD | Team_Vision | IBM_E2E | mean-p_miss@0.15rfa | 0.70872 |
2 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00083_Team-Vision_20181009-134800-1188.sr-20181009-134800-1731 | ActEV-2018 | AD | Team_Vision | IBM_E2E_ALL | mean-p_miss@0.15rfa | 0.71894 |
3 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00068_UCF_20181009-174811-7853.sr-20181009-174811-8151 | ActEV-2018 | AD | UCF | UCF | mean-p_miss@0.15rfa | 0.73299 |
4 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00074_BUPT-MCPRL_20181021-065538-7015.sr-20181021-065538-7426 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_ad | mean-p_miss@0.15rfa | 0.74854 |
5 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00077_BUPT-MCPRL_20181024-053432-9108.sr-20181024-053432-9511 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_aod2 | mean-p_miss@0.15rfa | 0.75078 |
6 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00080_BUPT-MCPRL_20181024-053231-6042.sr-20181024-053231-6330 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_ad3 | mean-p_miss@0.15rfa | 0.75078 |
7 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00076_BUPT-MCPRL_20181008-045311-3858.sr-20181008-045311-4151 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_ad2 | mean-p_miss@0.15rfa | 0.75078 |
8 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00075_BUPT-MCPRL_20181008-030636-8198.sr-20181008-030636-8583 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_aod | mean-p_miss@0.15rfa | 0.75556 |
9 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00097_Team-Vision_20181007-203040-8901.sr-20181007-203040-9533 | ActEV-2018 | AD | Team_Vision | IBM_E2E_ALL_NEW | mean-p_miss@0.15rfa | 0.75896 |
10 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00085_BUPT-MCPRL_20180928-002728-5146.sr-20180928-002728-5543 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_aod4 | mean-p_miss@0.15rfa | 0.75907 |
11 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00084_BUPT-MCPRL_20180928-002620-3845.sr-20180928-002620-4152 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_ad4 | mean-p_miss@0.15rfa | 0.75907 |
12 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00078_BUPT-MCPRL_20180921-234947-8187.sr-20180921-234947-8543 | ActEV-2018 | AD | BUPT-MCPRL | bupt_mcprl_aod3 | mean-p_miss@0.15rfa | 0.75907 |
13 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00067_IBM-MIT-Purdue_20180823-154949-8470.sr-20180823-154949-8999 | ActEV-2018 | AD | IBM-MIT-Purdue | IBM | mean-p_miss@0.15rfa | 0.83963 |
14 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00102_INF_20181009-100009-2955.sr-20181009-100009-3440 | ActEV-2018 | AD | INF | localresnettrnval_all | mean-p_miss@0.15rfa | 0.84398 |
15 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00099_INF_20181009-095825-5765.sr-20181009-095825-6169 | ActEV-2018 | AD | INF | localresnettrnval | mean-p_miss@0.15rfa | 0.87854 |
16 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00087_VANT_20181001-041813-0184.sr-20181001-041813-0488 | ActEV-2018 | AD | VANT | ACT | mean-p_miss@0.15rfa | 0.88228 |
17 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00094_DIVA-TE-Baseline_20181005-161350-3844.sr-20181005-161350-4153 | ActEV-2018 | AD | DIVA TE Baseline | baselineACT_1_AD | mean-p_miss@0.15rfa | 0.89457 |
18 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00103_DIVA-TE-Baseline_20181009-130747-6384.sr-20181009-130747-6737 | ActEV-2018 | AD | DIVA TE Baseline | baselineACTMerged_1_AOD | mean-p_miss@0.15rfa | 0.90599 |
19 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00104_DIVA-TE-Baseline_20181009-130834-9585.sr-20181009-130835-0175 | ActEV-2018 | AD | DIVA TE Baseline | baselineACT_1_AOD | mean-p_miss@0.15rfa | 0.90599 |
20 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00065_NIST-TEST_20181029-144922-2766.sr-20181029-144922-3544 | ActEV-2018 | AD | NIST-TEST | Test-System | mean-p_miss@0.15rfa | 0.90599 |
21 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00091_DIVA-TE-Baseline_20181002-140233-6845.sr-20181002-140233-7141 | ActEV-2018 | AD | DIVA TE Baseline | baselineRC3D_1_AD | mean-p_miss@0.15rfa | 0.90638 |
22 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00093_NII-Hitachi-UIT_20181115-024032-6192.sr-20181115-024032-6592 | ActEV-2018 | AD | NII_Hitachi_UIT | baseline | mean-p_miss@0.15rfa | 0.91994 |
23 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00086_NII-Hitachi-UIT_20181115-025013-5968.sr-20181115-025013-6297 | ActEV-2018 | AD | NII_Hitachi_UIT | trail | mean-p_miss@0.15rfa | 0.91994 |
24 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00081_NII-Hitachi-UIT_20181113-232655-7826.sr-20181113-232655-8173 | ActEV-2018 | AD | NII_Hitachi_UIT | init | mean-p_miss@0.15rfa | 0.92137 |
25 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00071_UTS-CETC_20181005-073137-6294.sr-20181005-073137-6625 | ActEV-2018 | AD | UTS-CETC | window2 | mean-p_miss@0.15rfa | 0.92491 |
26 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00088_USF-Bulls_20181008-095726-6807.sr-20181008-095726-7157 | ActEV-2018 | AD | USF Bulls | usf1 | mean-p_miss@0.15rfa | 0.93445 |
27 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00095_USF-Bulls_20181008-005831-9550.sr-20181008-005831-9931 | ActEV-2018 | AD | USF Bulls | usf2 | mean-p_miss@0.15rfa | 0.93445 |
28 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00098_USF-Bulls_20181009-164339-7900.sr-20181009-164339-8194 | ActEV-2018 | AD | USF Bulls | usf-3 | mean-p_miss@0.15rfa | 0.93445 |
29 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00066_USF-Bulls_20181008-013009-7242.sr-20181008-013009-7545 | ActEV-2018 | AD | USF Bulls | usf0 | mean-p_miss@0.15rfa | 0.93445 |
30 | ActEV-2018_AD_Phase-AD-ActEV-Leader_SYS-00072_UTS-CETC_20180907-024746-6012.sr-20180907-024746-6317 | ActEV-2018 | AD | UTS-CETC | base | mean-p_miss@0.15rfa | 0.95103 |
RANK | SUBMISSION_ID | EVALUATION_NAME | TRACK_NAME | TEAM_NAME | SYSTEM_NAME | METRIC_NAME | METRIC_VALUE |
---|---|---|---|---|---|---|---|
1 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00070_Team-Vision_20181009-140238-5458.sr-20181009-140238-6040 | ActEV-2018 | AOD | Team_Vision | IBM_E2E | mean-p_miss@0.15rfa | 0.75203 |
2 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00083_Team-Vision_20181009-134800-1188.sr-20181009-134800-1731 | ActEV-2018 | AOD | Team_Vision | IBM_E2E_ALL | mean-p_miss@0.15rfa | 0.76292 |
3 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00097_Team-Vision_20181007-203040-8901.sr-20181007-203040-9533 | ActEV-2018 | AOD | Team_Vision | IBM_E2E_ALL_NEW | mean-p_miss@0.15rfa | 0.77853 |
4 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00077_BUPT-MCPRL_20181024-053432-9108.sr-20181024-053432-9511 | ActEV-2018 | AOD | BUPT-MCPRL | bupt_mcprl_aod2 | mean-p_miss@0.15rfa | 0.78592 |
5 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00075_BUPT-MCPRL_20181008-030636-8198.sr-20181008-030636-8583 | ActEV-2018 | AOD | BUPT-MCPRL | bupt_mcprl_aod | mean-p_miss@0.15rfa | 0.84872 |
6 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00085_BUPT-MCPRL_20180928-002754-1535.sr-20180928-002754-1965 | ActEV-2018 | AOD | BUPT-MCPRL | bupt_mcprl_aod4 | mean-p_miss@0.15rfa | 0.85223 |
7 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00079_BUPT-MCPRL_20180921-235304-7372.sr-20180921-235304-7867 | ActEV-2018 | AOD | BUPT-MCPRL | bupt_mcprl_aod3 | mean-p_miss@0.15rfa | 0.85223 |
8 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00073_UCF_20180917-135516-1904.sr-20180917-135516-2312 | ActEV-2018 | AOD | UCF | UCF | mean-p_miss@0.15rfa | 0.93422 |
9 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00093_NII-Hitachi-UIT_20181115-024032-6192.sr-20181115-024032-6592 | ActEV-2018 | AOD | NII_Hitachi_UIT | baseline | mean-p_miss@0.15rfa | 0.93871 |
10 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00104_DIVA-TE-Baseline_20181009-130834-9585.sr-20181009-130835-0175 | ActEV-2018 | AOD | DIVA TE Baseline | baselineACT_1_AOD | mean-p_miss@0.15rfa | 0.94081 |
11 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00065_NIST-TEST_20181029-144922-2766.sr-20181029-144922-3544 | ActEV-2018 | AOD | NIST-TEST | Test-System | mean-p_miss@0.15rfa | 0.94081 |
12 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00081_NII-Hitachi-UIT_20181110-003804-3118.sr-20181110-003804-3450 | ActEV-2018 | AOD | NII_Hitachi_UIT | init | mean-p_miss@0.15rfa | 0.94247 |
13 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00101_INF_20181009-154733-3763.sr-20181009-154733-4392 | ActEV-2018 | AOD | INF | localresnettrnval_all | mean-p_miss@0.15rfa | 0.95099 |
14 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00103_DIVA-TE-Baseline_20181009-130747-6384.sr-20181009-130747-6737 | ActEV-2018 | AOD | DIVA TE Baseline | baselineACTMerged_1_AOD | mean-p_miss@0.15rfa | 0.96485 |
15 | ActEV-2018_AOD_Phase-AOD-ActEV-Leader_SYS-00100_INF_20181009-154712-8611.sr-20181009-154712-9045 | ActEV-2018 | AOD | INF | localresnettrnval | mean-p_miss@0.15rfa | 0.97125 |
Spatio-temporal detection of human activities in video is demanding in terms of labels and computation. In this talk, I will present recent work that attacks these problems. First, I will discuss supervision for activity detection from video-level class labels only. The state-of-the-art casts this weakly-supervised detection regime as a Multiple Instance Learning problem, where instances are a priori computed spatio-temporal proposals. Rather than disconnecting the spatio-temporal learning from the training, we propose Spatio-Temporal Instance Learning, which enables activity detection directly from box proposals in video frames. Then we will zoom in on representation learning for fully-supervised activity detection. The two-stream detection network based on RGB and flow provides state-of-the-art accuracy at the expense of a large model-size and heavy computation. We propose to embed RGB and optical-flow into a single two-in-one stream network with new layers. A motion condition layer extracts motion information from flow images, which is leveraged by the motion modulation layer to generate transformation parameters for modulating the low-level RGB features. The method is easily embedded in existing appearance- or two-stream action detection networks, and trained end-to-end. Experiments on several video datasets demonstrate the ability to detect human activities with less labels and computation, while maintaining competitive accuracy.
Coming Soon .
Accepted papers will be allocated 8 pages in the proceeding and that is, a paper can be up to 8 pages + the references. The manuscripts should be submitted in PDF format and should follow the requirements of the IEEE WACV paper format. All work submitted to WACV workshop is considered confidential until the papers appear. Accepted papers will be included in the Proceedings of IEEE WACV 2019 & Workshops and will be sent for inclusion into the IEEE Xplore digital library.
For WACV'19 Paper preparation and Author kit, See WACV'19 Submissions page for more details Paper preparation and Author kit.
Workshop Registration
You can register for HADCV'19 workshop at the WACV'19 registration page or by clicking this link: WACV2019 Registration