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QueryMamba: A Mamba-Based Encoder-Decoder Architecture with a Statistical Verb-Noun Interaction Module for Video Action Forecasting @ Ego4D Long-Term Action Anticipation Challenge 2024

Zeyun Zhong, Manuel Martin, Frederik Diederichs and Juergen Beyerer

Installation

conda install -c nvidia cuda-toolkit

conda env create -f environment.yaml python=3.9
conda activate mamba

Prepare data

Video features

This repo works on pre-extracted video features. You can download official ego4d features here or extract features by yourself.

Video features to clip features

Since a clip is much shorter than an entire video, we convert video features to clip features for faster I/O.

python tools.ego4d_video_to_clip_features.py

Generate action taxonomy and ground-truth annotations

We define action taxonomy as dictionary with (verb_label, noun_label) as the keys. The action taxonomy looks like:

{
    "0,10": {
        "verb": "adjust_(regulate,_increase/reduce,_change)", 
        "noun": "bag_(bag,_grocery,_nylon,_polythene,_pouch,_sachet,_sack,_suitcase)", 
        "freq": 28, 
        "action_label": 0
    },
    ...,
}

Each annotation file contains a Lx15 tensor, where L denotes the number of frames. 15 is the maximal number of labels for each frame. For instance, if a frame has the annotation [98,11,101,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1], then it has three "true" labels: 98, 11, and 101.

You can generate both action taxonomy and gt files with:

python tools.create_ego4d_gt_files.py

Data structure

After setting up features and gt files, your data structure may look like this:

Dataset root path (e.g., /home/user/datasets)
├── ego4d
│   └── version
│       │── annotations
│       │   └── action_taxonomy.json
│       │   └── fho_lta_train.json
│       │   └── ...
│       │── action_anno_perfram
│       │   └── clip_uid.pt
│       │── noun_anno_perfram
│       │   └── clip_uid.pt
│       │── verb_anno_perfram
│       │   └── clip_uid.pt
│       │── omnivore_video_swinl_clips
│       │   └── clip_uid.pt

Train / Test

All config params are defined in default_config.py.

bash expts/train_ego4d_querymamba.sh
# or testing
bash expts/test_ego4d_querymamba.sh