TIN

Abstract

For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process for spatial and temporal information. One natural question is whether we can embed the temporal information into the spatial one so the information in the two domains can be jointly learned once-only. In this work, we answer this question by presenting a simple yet powerful operator -- temporal interlacing network (TIN). Instead of learning the temporal features, TIN fuses the two kinds of information by interlacing spatial representations from the past to the future, and vice versa. A differentiable interlacing target can be learned to control the interlacing process. In this way, a heavy temporal model is replaced by a simple interlacing operator. We theoretically prove that with a learnable interlacing target, TIN performs equivalently to the regularized temporal convolution network (r-TCN), but gains 4% more accuracy with 6x less latency on 6 challenging benchmarks. These results push the state-of-the-art performances of video understanding by a considerable margin. Not surprising, the ensemble model of the proposed TIN won the 1st place in the ICCV19 - Multi Moments in Time challenge.

Citation

@article{shao2020temporal,
    title={Temporal Interlacing Network},
    author={Hao Shao and Shengju Qian and Yu Liu},
    year={2020},
    journal={AAAI},
}

Model Zoo

Something-Something V1

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	gpu_mem(M)	ckpt	log	json
tin_r50_1x1x8_40e_sthv1_rgb	height 100	8x4	ResNet50	ImageNet	44.25	73.94	44.04	72.72	6181	ckpt	log	json

Something-Something V2

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	gpu_mem(M)	ckpt	log	json
tin_r50_1x1x8_40e_sthv2_rgb	height 240	8x4	ResNet50	ImageNet	56.70	83.62	56.48	83.45	6185	ckpt	log	json

Kinetics-400

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb	short-side 256	8x4	ResNet50	TSM-Kinetics400	70.89	89.89	6187	ckpt	log	json

Here, we use finetune to indicate that we use TSM model trained on Kinetics-400 to finetune the TIN model on Kinetics-400.

:::{note}

The reference topk acc are got by training the original repo #1aacd0c with no AverageMeter issue.
The AverageMeter issue will lead to incorrect performance, so we fix it before running.
The gpus indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default.
According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU,
e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.
The inference_time is got by this benchmark script, where we use the sampling frames strategy of the test setting and only care about the model inference time,
not including the IO time and pre-processing time. For each setting, we use 1 gpu and set batch size (videos per gpu) to 1 to calculate the inference time.
The values in columns named after "reference" are the results got by training on the original repo, using the same model settings.
The validation set of Kinetics400 we used consists of 19796 videos. These videos are available at Kinetics400-Validation. The corresponding data list (each line is of the format 'video_id, num_frames, label_index') and the label map are also available.

:::

For more details on data preparation, you can refer to Kinetics400, Something-Something V1 and Something-Something V2 in Data Preparation.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train TIN model on Something-Something V1 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb.py \
    --work-dir work_dirs/tin_r50_1x1x8_40e_sthv1_rgb \
    --validate --seed 0 --deterministic

For more details, you can refer to Training setting part in getting_started.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test TIN model on Something-Something V1 dataset and dump the result to a json file.

python tools/test.py configs/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json

For more details, you can refer to Test a dataset part in getting_started.