Awesome
Long-Term Feature Banks for Detailed Video Understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick <br/> In CVPR 2019. [Paper] <br/> <br/>
<div align="center"> <img src="figs/lfb_concept_figure.jpg" width="800"> </img></div> <br/> This is a Caffe2 based implementation for our CVPR 2019 paper on Long-Term Feature Banks (LFB). LFB provides supportive information extracted over the entire span of a video, to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D CNNs with an LFB yields state-of-the-art results on AVA, EPIC-Kitchens, and Charades.Data Preparation and Installation
Please see DATASET.md, INSTALL.md for instructions.
Training and Inference
Please see GETTING_STARTED.md for details.
Results
The following documents a collection of models trained with this repository. Links to the trained models and output log files are provided. The performance is evaluated on the validation set.
Note that all models here are not the original models used in paper, but reproduced by this code base. The reproduced performance reported here is very close to (or slightly better than) what's reported in paper, but not exactly the same due to the stochastic nature of training.
AVA
config | backbone | method | mAP | model id | model |
---|---|---|---|---|---|
ava_r50_baseline | R50-I3D-NL | 3D CNN | 22.2 | 102760666 | model |
ava_r50_lfb_avg | R50-I3D-NL | LFB-Avg | 23.3 | 103505104 | model , lfb model |
ava_r50_lfb_max | R50-I3D-NL | LFB-Max | 23.9 | 103505159 | model , lfb model |
ava_r50_lfb_nl | R50-I3D-NL | LFB-NL-2L | 25.8 | 102824705 | model , lfb model |
ava_r50_lfb_nl_3l | R50-I3D-NL | LFB-NL-3L | 25.9 | 106403526 | model , lfb model |
ava_r101_baseline | R101-I3D-NL | 3D CNN | 23.2 | 102760714 | model |
ava_r101_lfb_nl_3l | R101-I3D-NL | LFB-NL-3L | 26.9 (multi-crop: 27.7) | 105206523 | model , lfb model |
EPIC Kitchens Verb
config | backbone | method | top1 | top5 | model id | model |
---|---|---|---|---|---|---|
epic_verb_r50_baseline | R50-I3D-NL | 3D CNN | 50.7 | 81.1 | 103704809 | model |
epic_verb_r50_lfb_avg | R50-I3D-NL | LFB-Avg | 52.9 | 82.5 | 103777391 | model , lfb model |
epic_verb_r50_lfb_max | R50-I3D-NL | LFB-Max | 53.3 | 81.0 | 103777432 | model , lfb model |
epic_verb_r50_lfb_nl | R50-I3D-NL | LFB-NL | 52.3 | 81.8 | 103777046 | model , lfb model |
EPIC Kitchens Noun
config | backbone | method | top1 | top5 | model id | model |
---|---|---|---|---|---|---|
epic_noun_r50_baseline | R50-I3D-NL | 3D CNN | 26.2 | 51.0 | 104421642 | model |
epic_noun_r50_lfb_avg | R50-I3D-NL | LFB-Avg | 29.1 | 56.3 | 103875866 | model |
epic_noun_r50_lfb_max | R50-I3D-NL | LFB-Max | 32.0 | 56.5 | 103875899 | model |
epic_noun_r50_lfb_nl | R50-I3D-NL | LFB-NL | 29.5 | 55.4 | 103706990 | model |
EPIC Kitchens Action
config | backbone | method | top1 | top5 |
---|---|---|---|---|
epic_verb_r50_baseline & epic_noun_r50_baseline | R50-I3D-NL | 3D CNN | 19.4 | 38.1 |
epic_verb_r50_lfb_avg & epic_noun_r50_lfb_avg | R50-I3D-NL | LFB-Avg | 21.2 | 41.3 |
epic_verb_r50_lfb_max & epic_noun_r50_lfb_max | R50-I3D-NL | LFB-Max | 22.9 | 41.2 |
epic_verb_r50_lfb_nl & epic_noun_r50_lfb_nl | R50-I3D-NL | LFB-NL | 21.8 | 40.5 |
Note: To make action predictions, we combine a verb model and a noun model, as opposed to training a separate action model. Performance in this table is computed using the verb/noun models from the tables above. Please see GETTING_STARTED.md for instructions on how to do this.
Charades
config | backbone | method | mAP | model id | model |
---|---|---|---|---|---|
charades_r50_baseline | R50-I3D-NL | 3D CNN | 38.3 | 102766107 | model |
charades_r50_lfb_avg | R50-I3D-NL | LFB-Avg | 38.4 | 102999065 | model , lfb model |
charades_r50_lfb_max | R50-I3D-NL | LFB-Max | 38.6 | 102999121 | model , lfb model |
charades_r50_lfb_nl | R50-I3D-NL | LFB-NL | 40.3 | 100866795 | model , lfb model |
charades_r101_baseline | R101-I3D-NL | 3D CNN | 40.4 | 103560426 | model |
charades_r101_lfb_avg | R101-I3D-NL | LFB-Avg | 40.8 | 103676713 | model , lfb model |
charades_r101_lfb_max | R101-I3D-NL | LFB-Max | 41.0 | 103676788 | model , lfb model |
charades_r101_lfb_nl | R101-I3D-NL | LFB-NL | 42.5 | 103641815 | model , lfb model |
License
Video-long-term-feature-banks is Apache 2.0 licensed, as found in the LICENSE file.
Citation
@inproceedings{lfb2019,
Author = {Chao-Yuan Wu and Christoph Feichtenhofer and Haoqi Fan
and Kaiming He and Philipp Kr\"{a}henb\"{u}hl and
Ross Girshick},
Title = {{Long-Term Feature Banks for Detailed Video Understanding}},
Booktitle = {{CVPR}},
Year = {2019}}