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set up python environment

virtualenv --system-site-packages -p python3 env_vtr

source env_vtr/bin/activate

pip install -r requirements.txt

Training self-supervised representation learning using a variation of the BarlowTwins

python -W ignore train_bt.py --n_epochs 100 --t_num_feats 512 --v_num_feats 2048 --batch_size_exp_min 7 --batch_size_exp_max 7 --lr_min 0.0001 --lr_max 0.001 --weight_decay_min 0.00001 --weight_decay_max 0.001 --lr_step_size_min 50 --lr_step_size_max 400 --lr_gamma 0.9 --relevance_score_min 0.00001 --relevance_score_max 0.0001 --shuffle

Training self-supervised representation learning and video-text model on MSRVTT dataset

python -W ignore train_msrvtt.py \
                    --n_epochs 500 \
                    --t_num_feats 512 \
                    --v_num_feats 2048 \
                    --loss_criterion cross_correlation \
                    --batch_size_exp_min 7 \
                    --batch_size_exp_max 10 \
                    --lr_min 0.000000001 \
                    --lr_max 0.00001 \
                    --weight_decay_min 0.0001 \
                    --weight_decay_max 0.1 \
                    --shuffle \
                    --patience 50 \
                    --max-target-loss 1000 \
                    --init-model-name experiment_loss_cross_correlation_lr_1e-05_wdecay_0.073226_bsz_256_1628194654

Test video-text performance on MSRVTT

python test_metrics_msrvtt.py \
    --batch-size 128 \
    --exp-name experiment_loss_cross_correlation_lr_9.5e-05_wdecay_0.007323_bsz_128_1628109801

Train video-text retrieval model

It initilaizes the vtr model using the video and text encoder weights from the SSL training. It loads the previously generated models at output/experiments/<exp_name>/model_v2r.sd and output/experiments/<exp_name>/model_t2r.sd

python train_vt.py
following is defunct

Train V2T model

python -W ignore train_v2t.py --n_epochs 500 --t_num_feats 512 --v_num_feats 2048 --batch_size_exp_min 7 --batch_size_exp_max 7 --lr_min 0.0001 --lr_max 0.001 --weight_decay_min 0.00001 --weight_decay_max 0.001 --lr_step_size_min 50 --lr_step_size_max 400 --lr_gamma 0.9 --relevance_score_min 0.4 --relevance_score_max 0.40001 --shuffle --loss_criterion mse

Train T2V model

python -W ignore train_t2v.py --n_epochs 500 --t_num_feats 512 --v_num_feats 2048 --batch_size_exp_min 7 --batch_size_exp_max 7 --lr_min 0.0001 --lr_max 0.001 --weight_decay_min 0.00001 --weight_decay_max 0.001 --lr_step_size_min 50 --lr_step_size_max 400 --lr_gamma 0.9 --relevance_score_min 0.4 --relevance_score_max 0.40001 --shuffle --loss_criterion mse

The resulting models will be stored at output/experiments

Evaluate metrics on test set

python test_metrics.py --test_split_path test.clean.split.pkl

Train the model with all the ae loss functions activated: python -W ignore train_dual_ae.py --n_epochs 10 --t_num_feats 512 --v_num_feats 2048 --batch_size_exp_min 4 --batch_size_exp_max 8 --lr_min 0.01 --lr_max 0.00001 --weight_decay_min 0.01 --weight_decay_max 0.00001 --lr_step_size_min 1 --lr_step_size_max 10 --activated_losses_binary_min 127 --activated_losses_binary_max 127

The train_v2t.py and train_t2v.py use a simple MLP model for video to text and text to video mapping. While the train_dual_ae.py trains two autoencoders simultaneously for v2t and t2v.

In order to evaluate the out of domain sentence detection model on Video Text Retireval (VTR) task, we have created a new presentation (format) of the Tempuckey dataset (in this repo) that generates (cuts) a short video segment per each sentence in the subtitles. This format allows us to create a Video Text Retreival model to retreive the videos using the sentences that the annotators say and vice versa. The video segments (one per sentence) are stored at /usr/local/data02/zahra/datasets/Tempuckey/sentence_segments/video_segments and the annotations (inclusing the sentences and their start/end time associated with each video) are stored at /usr/local/data02/zahra/datasets/Tempuckey/sentence_segments/sentences.pkl

The video and text features can be found at /usr/local/data02/zahra/datasets/Tempuckey/sentence_segments/feats

*note: some c3d video features are empty due to an issue with the feature extractor that relies on videos with a certain minimum length.

One-time process (no need to run the following again):

create sentence-clip correspondence dictionary (sentence segments)

generate_video_text_correpondence.py

generate_sentence_segments.py

generate clip segments (video cuts) for each sentence

generate_video_segments.py

extract video and text features for each clip and sentence

python3 extract_sent_feats.py

python3 extract_c3d_feats.py